orthoengine training manual

Geomatica OrthoEngineCourse Exercises Version 2013

Copyright and TrademarksGeomatica Version 2013

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Table of Contents

Geomatica OrthoEngine

Project files in OrthoEngine............................................................................................... 1-3

Geospatial data structures ................................................................................................ 1-4

Starting OrthoEngine ........................................................................................................ 1-7

Module 1: Project Setup

Lesson 1.1: Setting up satellite projects .............................................................................. 1-4

Checking the satellite orbital modeling workflow............................................................... 1-5

Creating a project.............................................................................................................. 1-6

Adding images to the project ............................................................................................ 1-8

Saving the project ............................................................................................................. 1-9

Lesson 1.2: Setting up aerial photograph projects............................................................. 1-11

Workflow for aerial photograph projects.......................................................................... 1-12

Creating a project............................................................................................................ 1-13

Adding the airphotos ....................................................................................................... 1-19

Collecting fiducial marks ................................................................................................. 1-20

Checkpoint ...................................................................................................................... 1-22

Module 2: Computing the math model

Checking the aerial photography project workflow............................................................ 2-2

Lesson 2.1: Collecting ground control points ....................................................................... 2-3

Collecting ground control points........................................................................................ 2-4

Collecting stereo GCPs................................................................................................... 2-15

Lesson 2.2: Collecting tie points ........................................................................................ 2-18

Collecting tie points manually.......................................................................................... 2-19

Collecting tie points automatically ................................................................................... 2-22

Checking the layout of the images.................................................................................. 2-25

Lesson 2.3: Calculating the math model............................................................................ 2-27

Understanding rigorous math models ............................................................................. 2-27

Computing the model ...................................................................................................... 2-28

Vector residual plots ....................................................................................................... 2-31

Reading the Residual Report .......................................................................................... 2-34

Checkpoint ...................................................................................................................... 2-36

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Table of Contents

Module 3: DEM operations

Lesson 3.1: Creating epipolar images ................................................................................. 3-2


Creating epipolar images .................................................................................................. 3-4

Lesson 3.2: Extracting and geocoding the DEM.................................................................. 3-7

Setting up to extract and geocode a DEM ........................................................................ 3-8

Editing the DEM .............................................................................................................. 3-14

Lesson 3.3: Building a DEM............................................................................................... 3-16

Checking the aerial photography project workflow.......................................................... 3-17

Setting up to build a DEM ............................................................................................... 3-18

Defining the output DEM file ........................................................................................... 3-20

Checkpoint ...................................................................................................................... 3-21

Module 4: Orthorectification


Lesson 4.1: Generating the orthorectified images ............................................................... 4-3

Setting up for orthorectification ......................................................................................... 4-4

Lesson 4.2: Adjusting orthorectified images ........................................................................ 4-9

Creating a new project to adjust orthorectified images ..................................................... 4-9

Adding a new ortho image to the Adjust ortho image project.......................................... 4-10

Collecting GCPs to adjust an ortho image ...................................................................... 4-11

Geometrically correcting an ortho image ........................................................................ 4-16

Viewing and performing quality control on an adjusted ortho image............................... 4-19

Clipping/subsetting an adjusted ortho image .................................................................. 4-20

Checkpoint ...................................................................................................................... 4-23

Module 5: Mosaicking


Lesson 5.1: Defining a mosaic Area .................................................................................... 5-4

Defining a mosaic area ..................................................................................................... 5-4

Selecting images for mosaicking....................................................................................... 5-7

Lesson 5.2: Manual mosaicking......................................................................................... 5-10

Mosaicking the first image............................................................................................... 5-10

Collecting and editing cutlines......................................................................................... 5-11

Adjusting the color balance............................................................................................. 5-20

Generating the mosaic .................................................................................................... 5-21

Lesson 5.3: Automatic mosaicking .................................................................................... 5-24

Defining a new mosaic area............................................................................................ 5-24

Mosaicking images automatically.................................................................................... 5-24

Generating the mosaic .................................................................................................... 5-29

Viewing the mosaic ......................................................................................................... 5-30

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Module 6: OrthoEngine componentization

Lesson 6.1: Data input and GCP collection ......................................................................... 6-2

Lesson 6.2: Project creation and tie point collection.......................................................... 6-13

Lesson 6.3: Automatic GCP collection and mosaicking..................................................... 6-20

Appendix A: Minimum GCP requirements

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Table of Contents

iv PCI Geomatics

Geomatica OrthoEngine

Welcome to the Geomatica OrthoEngine training course. This course is

designed for new and experienced users of remote sensing and digital

photogrammetry software.

In this course you will master the basics of GCP addition, tie point collection,

DEM extraction, orthorectification, and mosaicking. In addition, you will learn

about some of the newer features such as: OrthoEngine componentization,

running components in batch mode, automatic image-to-image registration,

and more.

There are six modules in this training manual. Each module contains lessons

that are built on basic tasks that you are likely to perform in your daily work.

They provide instruction for using the software to carry out essential processes

while sampling key OrthoEngine applications and features.

About this manual

The scope of this guide is confined to the core tools available in Geomatica

OrthoEngine; however, some remote sensing concepts are reviewed in the

modules and lessons.

Each module in this book contains a series of hands-on lessons that let you

work with the software and a set of sample data. Lessons have brief

introductions followed by tasks and procedures in numbered steps.

The following modules are included in this course:

• Module 1: Project Setup

• Module 2: Computing the math model

• Module 3: DEM operations

• Module 4: Orthorectification

• Module 5: Mosaicking

• Module 6: OrthoEngine componentization

Introduction

PCI Geomatics 1

Geomatica OrthoEngine - Introduction: Geomatica OrthoEngine

Module 1 and Module 2 deal with the Data Preparation stage and include exercises

for setting up a project, loading images, adding ground control points (GCPs),

collecting tie points (TPs), applying sensor models, and examining reports.

Module 3 is concerned with Data Extraction.

Module 4 and Module 5 deal with the Data Correction stage and include lessons

for setting up and generating ortho images, defining a mosaic area, manual

mosaicking, and automatic mosaicking.

Module 6 examines how to set up and run OrthoEngine components in batch mode

using Modeler.

The data you will use in this course can be found in the OE Data folder supplied on

the accompanying CD. You should copy this data to your hard disk.

Note

This training manual can be used to set up any kind of image or airphoto project. Modules 2 through 6 apply to any kind of data. Substitute the file names in the manual with your own data file names. The OrthoEngine Workbook will allow you to explore other math models using a variety of datasets.

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Project files in OrthoEngine

To use OrthoEngine, a project file must be created. Project files are ASCII text files with a .prj extension. When you create a project, you specify a math model, the mathematical relationship used to correlate the pixels of an image to correct locations on the ground accounting for known distortions. You also specify the coordinate system and a datum for the project. All data in the project must use this coordinate system and datum.

A typical project file contains:

• Project information

• Camera calibration

• Projection setup

• Photo or image information including the:

• File name and location of each input photo or image

• File name of the output ortho

• File name of the DEM associated with the ortho

• Background value for the DEM

• Photo or image channel where data is stored

• Ortho channel where data is stored

• Clip area coordinates

• Status of the bundle adjustment

• Status of the ortho

• Fiducial mark and principal point locations for all photos

• Ground control point and tie point locations with elevation data

• Exterior orientation values

The next three sections of the project file list information about:

• Cutlines created in the mosaicking step

• Look-up tables generated to match the images radiometrically during the mosaicking step

• Preferences set up for the appearance of the cursors and labels for such things as GCPs and TPs associated with the working photo or image

The final section of the project file lists the parameters that were set up during the ortho generation step. The information includes the following:

• Elevation units

• Resampling method used for the orthorectification

• Amount of memory allocated for the orthorectification

• Output resolution

• Output mosaic file name

• Upper left bounds of the final mosaic

• Lower right bounds of the final mosaic

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Geospatial data structures

Data for geospatial applications are stored in complex files that are often incompatible with specific software packages and operating systems. Files can come in hundreds of different formats and in most geospatial applications often require considerable preparation or preprocessing before they can be combined in a work project.

Most geospatial formats store image data in one file and supplementary data, such as bitmaps, vector layers and metadata in another file using different file extensions for each data type. Updating and maintaining complex datasets made up of many file types can be a difficult and error-prone process.

PCI Geomatics has developed two unique technologies that make data management easier: Generic Database (GDB) technology and the PCIDSK file format. The following sections explain how GDB technology and the PCIDSK format work in Geomatica to make your data management easier.

GDB technology in Geomatica

Generic Database (GDB) technology is key to Geomatica applications. GDB makes it possible to view and integrate geospatial data from more image formats than any other geomatics software. It allows you to use as much data as you require in your work and to combine images of any data type, resolution, and size. You can use image files, with their accompanying metadata, in the same georeferenced viewer even after combining various file formats and data types.

The list of file formats that GDB uses is constantly under development; there are currently close to 150 usable geospatial file types. Many popular formats such as ARC/INFO, GeoTIFF, JPEG2000, AutoCAD, and MicroStation are fully supported.

GDB operates behind the scenes in Geomatica applications. The illustration below shows a file selection window for Geomatica Focus. When you click the Files of type box, you can see the list of file formats that can be opened directly into a Geomatica application.

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Figure 1 GDB in Geomatica

With GDB technology, you can work through a mapping project by assembling raster and vector data from different sources and different file formats without the need to preprocess or reformat the data. Together, GDB and Geomatica read, view, and process distribution formats, and read, edit, and write exchange formats.

PCIDSK and Geomatica

PCIDSK files contain all of the features of a conventional database and more. They store a variety of data types in a compound file that uses a single file name extension. The image data are stored as channels and auxiliary data are stored as segments. All data types are stored together in the file using .pix as the file name extension. The data type and format of the component determines whether searching, sorting and recombining operations can be performed with the software application tools.

In PCIDSK files, images and associated data, called segments, are stored in a single file. This makes it easier to keep track of imagery and auxiliary information.

PCIDSK file format

Using a single file for each set of data simplifies basic computing operations. Since all data is part of the same file you can add or remove parts of it without having to locate, open, and rename more files.

PCIDSK files are identical in all operating environments and can be used on networked systems without the need to reformat the data.

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Figure 2 Conventional files and PCIDSK files

PCIDSK Files Conventional Files

Image Files

Training site files

Histogram files

Image channels

Training site segments

Histogram segments

Saved Separately using differentSaved as a single file using the file namefile name extensionsextension .pix

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Starting OrthoEngine

Windows systems

To start OrthoEngine on Windows systems:

1. Click the Start button, click Programs, click PCI Geomatics, click Geomatica Vx.x and then click OrthoEngine.

Alternatively, if the Geomatica Toolbar is running, click the OrthoEngine button.

The OrthoEngine window opens.

Unix systems To start OrthoEngine on Unix systems:

1. Enter the Unix environment.

2. At the command prompt, type orthoeng.

Alternatively, click the OrthoEngine icon on the Geomatica Toolbar.

Figure 3 OrthoEngine window

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Project Setup

Module 1: has two lessons:

Lesson 1.1 Setting up a Satellite project

Lesson 1.2 Setting up an Airphoto Project

Data preparation stage

The Data Preparation stage consists of the following modules:

• Module 1: Project Setup


In Module 1, you learn how to set up your project by:

• Selecting the math model

• Specifying the projection information

• Adding your images to the project

• Saving your project

Starting a Project To start a new project you need to select a math model. A math model is a

mathematical relationship used to correlate the pixels of an image to correct

locations on the ground accounting for known distortions. The math model that

you choose directly impacts the outcome of your project. To achieve the results

that you are looking for, you need to understand what the math models do,

what the math models require to produce an acceptable solution, and which

math model to use with your project. You can use one of six math modeling

methods:

• Aerial Photography

• Optical Satellite Modeling

• Radar Satellite Modeling

• Polynomial

• Thin Plate Spline

• Adjust Orthos

• None (mosaic only)

The OrthoEngine Workbook includes exercises and detailed descriptions of the options available for the different math models.

Module

1

PCI Geomatics 1-1

Geomatica OrthoEngine - Module1: Project Setup

In this manual, an Aerial Photography Modelling project is used to examine the Data Preparation, Data Extraction and Data Correction stages of an OrthoEngine project.

Table 1.1: Math Modelling Methods available in OrthoEngine

Project Stage Aerial Photography

Satellite - Toutin’s Model

Satellite - ASAR/PALSAR/RADARSAT Specific Model

Satellite - Low Resolution: AVHRR

Data Preparation

Digital and analog photos supported

Camera calibration information is required

Exterior orientation can be calculated from GCPs/TPs, GCPs/TPs and GPS/INS, or with GPS/INS only

Sensor model is calculated for the block of air photos in the project

Images must be read to pix format - orbital segment is created within the pix file

A minimum number of GCPs must be collected to calculate the sensor model

Tie points can be collected for overlapping images

Sensor model is calculated for the block of images


GCPs are optional, 1 or 2 can be collected to improve the model

Tie points cannot be collected

Sensor model is calculated for each individual image in the project




Data Extraction

Import and build DEM options are available

A DEM can be extracted from stereo photos

3D viewing and 3D feature extraction possible with stereo photos


A DEM can be extracted from stereo images

3D viewing and 3D feature extraction possible with stereo images





Data Correction

A raster DEM is required for orthorectification

Manual and automatic mosaicking





Orthorectification performed within PCI GeoComp

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Polynomial Thin Plate Spline

Rational Functions - Compute from GCPs

Rational Functions - Extract from image file

Mosaic Only

Any digital image in a GDB supported format can be input into a project

GCP coordinate contains only x and y values




GCP coordinate must have x, y and z values




GCP coordinate must have x, y and z values



Input files in GeoTiff or NITF formats with associated RPC metadata


Tie points can be collected for overlapping images

Sensor model is calculated for block of images


No sensor model is calculated as input images are already georeferenced






Extracted elevations referenced to ellipsoidal heights

No DEM needed for geometric correction


No DEM needed for geometric correction







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Lesson 1.1 Setting up satellite projects

In this lesson you will:

• Create a project

• Set projection parameters

• Add the data to the project

• Save your project

This lesson describes how to set up a pair of satellite images as part of the Data

Preparation stage. To set up your images, you require:

• Optical images in their raw data format, or

• Radar images in their raw data format

• Map projection information

Note

The processing steps for optical and radar projects are the same.

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Checking the satellite orbital modeling workflow

PCI Geomatics 1-5


Creating a project

OrthoEngine works on a project-by-project basis. Therefore, you need to open an

existing project or create a new project before you gain access to the functions

within OrthoEngine.

In this lesson, you will set up a new project using optical data. The procedures are the same for working with radar data.

To create a new project:

1. On the OrthoEngine window in the File menu, click New.

The Project Information window opens.

2. Click Browse.

The File Selector window opens.

3. Locate the SPOT folder.

4. In the File name box, enter spot.prj and click Open.

The path and filename appear in the File name box in the Project Information window.

5. In the Name box, enter SPOT Project.

6. In the Description box, enter SPOT ortho project for Irvine, CA.

7. For the Math Modelling Method, select Optical Satellite Modelling.

8. Under Options, select Toutin’s Model.

Note that SPOT is listed in this category.

9. Click OK.

The Project Information window closes and the Set Projection window opens.

Figure 1-1: Project Information window

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Figure 1-2: Set Projection window

Setting the projection parameters

A projection is a method of portraying all or part of the earth on a flat surface.

Output Projection defines the final projection for orthoimages, mosaics, 3-D

features, and digital elevation models (DEMs).

GCP Projection defines the projection of your source of ground control information

used during either manual ground control point (GCP) collection or when importing

GCPs from text file. If you collect GCPs from a geocoded source, the coordinates

are reprojected to the GCP Projection and saved into the project file.

If you collect GCPs from multiple sources, you can change the GCP Projection to

match each source using the Set Projection window. Using different projections

increases processing time during orthorectification, but it means that you do not

have to reproject your ground control prior to using it in OrthoEngine.

The projection information needs to be set at the beginning of each project. In the Set Projection window, enter the projection information for the Irvine area.

Output Projection

To enter the Output Projection parameters:

1. From the list to the left of the Earth Model button, select UTM.

The Earth Models window opens.

2. Click the Ellipsoids tab.

3. Select E000 and click Accept.

The UTM Zones window opens.

4. Select Zone 11 and click Accept.

The UTM Rows window opens.

5. Select Row S and click Accept.

6. In the Output pixel spacing box, type 10.

7. In the Output line spacing box, type 10.

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GCP Projection To enter the GCP Projection parameters:

1. Under GCP Projection, click Set GCP Projection based on Output Projection.

The GCP Projection adopts the same settings used for the Output Projection.

2. Click OK.

The Set Projection window closes.

Tip

If you wish to modify the projection information, reopen the Set Projection window.

Warning

Changes to the projection mid-project will make any existing orthophotos invalid.

Adding images to the project

For most sensors, OrthoEngine uses the Read CD-ROM option on the Data Input

toolbar to read the raw satellite data, save the imagery into a PCIDSK file, and add

a binary segment containing the ephemeris data (orbit information) to the file.

Caution

If you save satellite data from the CD onto a hard disk before reading it to a PCIDSK file, it is important that you maintain the naming structure of the folders as they appeared on the CD. If the structure or folder names are changed, you may encounter errors.

For this lesson, the data have already been read to PCIDSK format. In this case, you will use the Read PCIDSK file option from the Data Input toolbar.

To import satellite data from a PCIDSK file:

1. On the OrthoEngine window in the Processing step list, select Data Input.

A new toolbar with four icons appears on the OrthoEngine window. With the Data Input toolbar, you can input data from either CD-ROM, PCIDSK file, or a generic image file.

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Figure 1-3: Data Input toolbar

2. On the Data Input toolbar, click Read PCIDSK file.

The Open Image window opens.

3. Make sure the Uncorrected images option is selected.

4. Click New Image.


5. Locate the SPOT folder.

6. Hold down the CTRL key, select spotleft.pix and spotright.pix and click Open.

The Multiple File selector message window opens. This window indicates the total number of files that are detected, and the total number to be loaded into the project.

7. Click OK.

The File Selector window closes. Both SPOT images are now part of your project.

Saving the project

To save your project file:

• From the File menu in the OrthoEngine window, click Save.

The spot.prj file is saved in the SPOT folder.

In addition, OrthoEngine automatically creates a backup file every 10 minutes. The backup file uses the same file name as your project file, but with a .bk extension.

Tip

If you need to revert to the backup file, rename the backup file so that it uses the .prj extension. OrthoEngine can load this project file in the normal way.

To change the settings of the backup option:

1. On the OrthoEngine window, click the Options menu and select Auto Backup.

2. Type the number of minutes that you want between backups.

3. Click Close.

PCI Geomatics 1-9


In this lesson you:

• Created a project


• Added the data to the project

• Saved your project

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Lesson 1.2 Setting up aerial photograph projects



• Create a project


• Enter camera calibration information

• Add the airphotos to the project

• Collect fiducial marks

• Save your project

This lesson describes how to prepare four airphotos as part of the Data Preparation

stage. To set up your airphotos, you require:

• Aerial photographs with camera calibration data

• Map projection information

Term

Airphoto is short for aerial photograph. Aerial photograph, in the broadest sense, means a photograph taken from an airborne platform.

Term

In this lesson, you deal with strip photographs. Strip photography refers to a number of consecutive overlapping photos taken along a flight line, usually at a constant altitude.

PCI Geomatics 1-11


Workflow for aerial photograph projects

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Creating a project

OrthoEngine works on a project-by-project basis. Therefore, you need to open an

existing project or create a new project before you gain access to the functions

within OrthoEngine.

In this lesson, you will set up a new project using four aerial photographs data.

To create a new project:

1. On the OrthoEngine window in the File menu, click New.

The Project Information window opens.

2. Click Browse.


3. Locate the AIRPHOTO folder.

4. In the File name box, enter airphoto.prj and click Open.

The path and filename appear in the File name box in the Project Information window.

5. In the Name box, enter Airphoto Project.

6. In the Description box, enter Airphoto ortho project for Richmond Hill, ON.

7. For the Math Modelling Method, select Aerial Photography.

Camera Type Select the type of camera in the Options area of the Project Information window. The options are:

• A Standard Aerial camera

• A Digital/Video camera

The photos used in this lesson were taken with a Standard Aerial camera.

Tip

If your photos were taken with a Digital/Video camera, refer to the Digital Airphoto exercise in the OrthoEngine Workbook for further details.

8. In the Camera Type section, select Standard Aerial.

PCI Geomatics 1-13


Exterior Orientation

The exterior orientation is:

• Computed from ground control points and tie points, or

• Provided by the user

Many aircraft are equipped with onboard Global Positioning Systems (GPS), and sometimes with Inertial Navigation Systems (INS). These systems collect the exterior orientation of the camera directly on the aircraft. Select User Input to use the GPS and INS readings alone and accept them as correct. Select Compute from GCPs and Tie Points to use ground control points and/or tie points to refine the GPS and INS results.

Term

Whereas the interior orientation defines the relationship between the camera and the image, the exterior orientation defines the relationship between the camera and Earth. Specifically, the exterior orientation defines the spatial position and angular orientation of a photo.

9. In the Exterior Orientation section, select Compute From GCPs & Tie Points.

10. Click OK.

The Project Information window closes and the Set Projection window opens.

Figure 1-4: Project Information window after the information is entered

Tip

If you wish to modify your project information at any time, reopen the Project Information window. However, you can not change the math model once it has been set.

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Figure 1-5: Set Projection window

Setting the projection parameters

A projection is a method of portraying all or part of the earth on a flat surface.

Output Projection defines the final projection for orthoimages, mosaics, 3-D

features, and digital elevation models (DEMs).

GCP Projection defines the projection of your source of ground control information

used during either manual ground control point (GCP) collection or when importing

GCPs from text file. If you collect GCPs from a geocoded source, the coordinates

are reprojected to the GCP Projection and saved into the project file.

If you collect GCPs from multiple sources, you can change the GCP Projection to

match each source using the Set Projection window. Using different projections

increases processing time during orthorectification, but it means that you do not

have to reproject your ground control prior to using it in OrthoEngine.

The projection information needs to be set at the beginning of each project. In the Set Projection window, enter the projection information for the Richmond Hill, Ontario area.

Output Projection

To enter the Output Projection parameters:

1. From the list to the left of the Earth Model button, select UTM.

The Earth Models window opens.

2. Click the Ellipsoids tab.

3. Select E012 and click Accept.

The UTM Zones window opens.

4. Select Zone 17 and click Accept.

The UTM Rows window opens.

5. Select Row T and click Accept.

6. In the Output pixel spacing box, type 0.4.

7. In the Output line spacing box, type 0.4.

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GCP Projection To enter the GCP Projection parameters:

1. Under GCP Projection, click Set GCP Projection based on Output Projection.

The GCP Projection adopts the same settings used for the Output Projection.

2. Click OK.

The Set Projection window closes and the Standard Aerial Camera Calibration Information window opens.

Tip

If you wish to modify the projection information, reopen the Set Projection window.

Warning

Changes to the projection mid-project will make any existing orthophotos invalid.

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Entering the camera calibration data

The camera calibration data is used to identify and correct the distortions

introduced into the image due to the curvature of the lens, the focal length, and the

perspective effects. This information is used to compute the interior orientation,

which is the relationship between the film and the camera.

Images taken with a standard photogrammetric aerial camera usually come with a

report that provides data about the camera.

Focal Length The Focal Length is the distance between the focal point of the lens and the film. Entering an incorrect focal length may introduce unwanted distortions in your project. This is a compulsory parameter.

Radial Lens Distortion

Radial Lens Distortion is the symmetric distortion caused by the lens due to imperfections in curvature when the lens was ground. In most cases, the errors introduced by radial lens distortion (around 1 to 2 um) are much smaller than the scanning resolution of the image (around 25um). Entering the values may significantly increase the processing time while contributing very little value to the final product. The values for the Radial Lens Distortion may be provided to you as R0 through R7 coefficients or in tabular format. These parameters are optional and the coefficients may or may not appear in the camera calibration report.

If you are using a USGS camera calibration report, the coefficients are given as K0, K1, K2, K3 and K4, which correspond to R1, R3, R5, and R7. K4 is discarded since it is usually zero.

Fiducial Marks Fiducial marks are small crosses or small V-shaped indents located precisely on each of the four corners and/or exactly midway along the four sides of a standard aerial photograph. After you identify the fiducial marks in your scanned image, OrthoEngine uses the fiducial marks entered from the camera calibration report to establish an image coordinate frame. The fiducial mark coordinates are a compulsory parameter for standard aerial photographs.

Photo Scale Image Scale is the ratio of the size of the objects in the image to the size of the objects on the ground. This parameter is optional, except when you want to import GPS/INS observations and use them during the automatic tie point measurements.

Entering the incorrect Image Scale may cause the computation of the math model (the bundle adjustment) to fail.

Earth Radius The Earth Radius is the radius of curvature of the earth at the location of the project. This parameter is optional since aerial photographs usually use a large scale (for example, 1:8,000) and the error due to the earth's radius is negligible. You only need earth radius correction for images with a scale over 1:20,000.

PCI Geomatics 1-17


To enter the camera calibration data:

1. In the Standard Aerial Camera Calibration Information window, enter the information shown in Table 2.

2. After all the data is entered, click OK.

Figure 1-6: Standard Aerial Camera Calibration Information window

Tip

You can modify the camera calibration information at any time by reopening the Standard Aerial Camera Calibration Information window.

Table 1.2: Camera Calibration Data

Focal Length 152.856

Corner Fiducial Marks X Y

Top Left -106.000 106.000

Top Right 106.000 106.000

Bottom Right 105.996 -106.000

Bottom Left -105.996 -106.000

Image Scale 1:8000

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Adding the airphotos

This section describes how to add the airphotos to the project file. The project file will then contain the filename and location of each input photo.

To import images into the project:

1. On the OrthoEngine window in the Processing Step list, select Data Input.

A new toolbar with six icons appears on the window.

Figure 1-7: Data Input toolbar

2. On the Data Input toolbar, click Open a new or existing image.


3. Click New Image.


4. Locate the AIRPHOTO folder.

5. Press the CTRL key and select files S129.pix, S130.pix, S188.pix and S189.pix and click Open.

The Multiple File selection message window opens indicating the total number of files that are detected and are to be loaded into the project.

6. Click OK.

The four photos are listed in the Open Image window.

To open the first photo:

1. In the Open Image window, select S129.pix and click Open.

A viewer opens displaying photo S129.pix. In addition, the Fiducial Mark Collection window opens for this photo.

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Figure 1-8: Viewer showing photo S129.pix

Figure 1-9: Fiducial Mark Collection window

Collecting fiducial marks

OrthoEngine links the fiducial mark coordinates entered from the camera calibration report to the positions that you identify on the scanned image. You must identify the fiducial marks in every image.

Tip

If you are working in a project with a large volume of images, it is recommended that you enter the fiducial marks and ground control points for a limited number of images (up to five), complete the calculation of the math model, and then check for errors before continuing. It is easier to locate bad points on a few images than over the entire project.

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Manual collection

To manually collect fiducial marks:

1. Click the approximate location of the fiducial mark in the top left corner, using the zoom tools as necessary.

A red crosshair appears in the viewer.

2. Click precisely in the center of the fiducial mark.

3. In the Fiducial Mark Collection window, click Set beside the Top left pixel and line boxes.

The Pixel and Line coordinates for the fiducial mark appear in the window.

4. Repeat steps 1 to 3 to collect fiducial marks in the Top right, Bottom right and Bottom left corners.

5. For the Calibration Edge, select Left.

This is the position of the data strip as it appears in the image on the screen.

Errors Under Errors, OrthoEngine compares the computed fiducial mark positions based on the measurements taken from the screen with the fiducial information that you entered from the camera calibration report. Click Clear beside any fiducial marks where the error is not acceptable and repeat the collection process.

The error should be less than one pixel, unless the image is scanned at a very high resolution. Large errors may indicate that either the coordinates from the camera calibration report were entered incorrectly or the fiducial mark was collected incorrectly from the scanned image.

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Automatic collection

After collecting the fiducial marks manually for one of your images, OrthoEngine can use automated pattern matching to automatically collect the fiducial marks for the rest of your images in the project.

To automatically collect fiducial marks for the remaining photos:

1. After manually collecting fiducials for the first photo, click Auto Fiducial Collection.

A window opens, that asks Do you want to overwrite photos with fiducial marks?

2. Click No.

This will use the pattern matching only on images without measured fiducial marks.

3. After the Progress Monitor closes, click OK.

4. To accept the fiducial marks, click OK.

The Fiducial Mark Collection window closes.

You can verify the accuracy of the fiducial mark collection by viewing the fiducial.rpt report in the folder where the project is saved.

Tip

This is a good time to save your project file.

To save your project file:

• From the File menu in the OrthoEngine window, click Save.

The airphoto.prj file is saved in the AIRPHOTO folder.

In this lesson you:

• Created a project


• Entered camera calibration information

• Added the airphotos to the project

• Collected fiducial marks

• Saved your project

Checkpoint

You are now ready to proceed to Module 2: Computing the math model. In Module

2, you collect ground control points and tie points for your project, and then

calculate the math model by way of a bundle adjustment.

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Computing the math model

Module 2 has three lessons:

Lesson 2.1 Collecting ground control points

Lesson 2.2 Collecting tie points

Lesson 2.3 Calculating the math model

Data preparation stage

The Data Preparation stage consists of the following modules:

• Module 1: Setting Up Images and Photos


In Module 2, you learn how to:

• Collect ground control points

• Collect tie points

• Calculate the math model

Term

The computation of a rigorous math model is often referred to as a bundle adjustment. The math model solution calculates the position and orientation of the sensor - the aerial camera or satellite - at the time the image was taken. Once the position and orientation of the sensor is identified, it can be used to accurately account for known distortions in the image.

Module

2

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Geomatica OrthoEngine - Module2: Computing the math model

Checking the aerial photography project workflow

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• Collect GCPs from a geocoded image

• Import GCPs from a text file

• Collect stereo GCPs

To add GCPs, you require:

• The airphoto.prj project file from Module 1 for the aerial photographs S129.pix, S130.pix, S188.px and S189.pix.

• The air_mos.pix mosaic file contained in the AIRPHOTO folder, which serves as the georeferenced image.

Term

A ground control point (GCP) is a feature that you can clearly identify in the raw image for which you have a known ground coordinate.

Ground coordinates can come from a variety of sources such as the Global

Positioning System (GPS), ground surveys, geocoded images, vectors,

Geographic Information Systems (GIS), topographic maps, chip databases, or by

using photogrammetric processes to extend the number of GCPs in your images.

A GCP determines the relationship between the raw image and the ground by

associating the pixel (P) and line (L) image coordinates to the x, y, and z

coordinates on the ground.

Although the media, formats, and methods used to collect the coordinates are

different depending on the source, the idea is the same. You have to match a point

in the raw image to a set of coordinates.

Since some sources of ground control only offer dispersed points, it may be more

efficient to select a point in the source first and then locate it in the raw image. For

example, a vector file may have a limited number of features available as ground

control compared with a geocoded image.

Collecting good GCPs

• Select features that can be identified accurately at the resolution of the raw image.

• Select features that are close to the ground. Because elevated features in the image will appear to “lean”, selecting features on the ground will ensure that the point is not displaced from the actual ground coordinate.

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• Avoid picking shadows. These are easy to see in the image, but they are not permanent features and can move from one image to another.

• Avoid repetitive features such as parking lots and lines on a highway, since it is easy to select the wrong one.

• When collecting GCP coordinates in the field (via GPS or survey), try to identify good targets in the raw image before arbitrarily collecting coordinates in the field.

• GCPs should be collected in a wide distribution over the image and the project. Ensure that the GCPs are collected from a variety of ground elevations.

• A GCP may be selected on a single image, or may be selected in an area of overlap between 2 or more images. GCPs selected in multiple images help to produce a more accurate model.

How many GCPs?

The minimum number of GCPs you need to collect depends on the type of data you

are correcting, the processing level of that data, and which math model you are

using. For more information, please refer to Appendix A.

For the Aerial Photography model, the minimum requirement is that you have at

least two GCPs on at least one photo in the project, to ensure scale. However,

there should be a few photos with 3 GCPs in the project. This provides correct

levelling and scale for the math model. Tie points can hold the rest of the project

together.

Tip

If you are working in a project with a large volume of images, it is recommended that you enter the ground control points for a limited number of images (up to five), complete the calculation of the math model, and then check for errors before continuing. It is easier to locate bad points on a few images than over the entire project.

Collecting ground control points

If you have several images open, one image resides in a viewer labeled Working

while the others are labeled Reference. The GCP Collection window collects and

displays the GCPs from the image in the Working viewer only. Click the Reference

button to switch the viewer to Working.

You will now open S129.pix as the Working Image.

To open the Working Image:

1. On the OrthoEngine window in the Processing step list, select GCP/TP Collection.

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A new toolbar with seven icons appears. The icons on the toolbar are shortcuts to all the functions you need during GCP and TP collection.

Figure 2-1: GCP/TP Collection toolbar

2. On the GCP/TP Collection toolbar, click Open a new or existing image.

The Open Image window opens listing the four photos in this project.

3. Select S129.pix and click Open.

A viewer opens containing the S129.pix photo as the Working Image.

Figure 2-2: Viewer containing S129 as the Working Image

Collecting GCPs from a geocoded imageBefore you begin collecting ground control points on the Working Image, you need to load the geocoded image.

To load the geocoded image:

1. On the GCP/TP Collection toolbar, click Collect GCPs Manually.

The GCP Collection for S129 window opens.

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Figure 2-3: GCP Collection window

2. From the Ground control source list, select Geocoded image.

A File Selector window opens automatically.

3. From the AIRPHOTO folder, select air_mos.pix and click Open.

The air_mos.pix file is loaded in a viewer as the Geocoded Image and is listed at the top of the GCP Collection window.

If you chose the Aerial Photography, Satellite Orbital, Rational Functions, or Thin

Plate Spline math models, you can use a digital elevation model (DEM) to

determine the elevation of your GCPs. The DEM does not have to be in the same

projection as the source of the GCPs.

To load the DEM to set elevation:

1. Beside the DEM box, click Browse.

2. From the AIRPHOTO folder, select ap_dem.pix and click Open.

The DEM File window opens where you select the channel containing the DEM information. This will be your source of elevation for your ground control points.

3. Enter a Background elevation of -150 and click OK.

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Background elevation

Background elevation represents those areas inside the DEM for which there is no data provided. For DEMs generated by OrthoEngine, the background elevation defaults to -150. Other DEMs have different background elevation values that you must know before they can be used.

Tip

If you do not know the background value, click DEM Info in the DEM File window. The window displays the three lowest and three heights values in the DEM.

To collect GCPs from a geocoded image:

1. In the air_mos.pix viewer, place the crosshairs near the left edge of the image at the position shown in the figure below.

Figure 2-4: Location of first GCP (circled in black)

2. Place the cursor on the location shown in the figure below, zooming in as necessary.

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Figure 2-5: Location of crosshairs for G0001 (circled in black)

3. In the Geocoded Image viewer, click Use Point.

The georeferenced coordinates for this location are transferred to the GCP Collection window. They should be approximately:

627260 X

4857514 Y

4. In the GCP Collection window, click Extract Elevation.

5. Place the crosshairs on the same feature in the uncorrected S129.pix photo.

6. When you are satisfied with the position of the crosshairs, click Use Point.

The image coordinates for G0001 are transferred to the GCP Collection window. They should be approximately:

320 Pixel

2257 Line

7. In the GCP Collection window, click Accept.

The GCP information is transferred to the Accepted Points list for the GCP with Point ID G0001.

Term

The Point ID is a label automatically assigned to each GCP. You can type a new label in the Point ID box, however, all points (ground control points, independent check points, tie points, and elevation match points) in the image must have unique labels. When collecting stereo GCPs (the same GCP in the overlap areas of different images), use the same Point ID in each image.

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Note

The same workflow is used when collecting GCPs from geocoded vectors.

Tip

You can edit the error estimate in the +/- boxes to correspond to your ability to precisely identify a feature in the image. For example, if you use coarse imagery, you can probably only measure to the closest pixel. If you use imagery that was compressed or poorly scanned, you may only be able to measure to the closest two pixels. Even if you identify a GCP to the closest pixel, the coordinate may only be accurate to a certain number of meters.

To collect the second GCP:

• Follow steps 1 to 7 above to collect a GCP at the location shown in Figure 2-6: below.

Figure 2-6: Location of G0002 on air_mos.pix

To collect the third GCP:

• Follow steps 1 to 7 above to collect a GCP at the location shown in the figure below.

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Figure 2-7: Location of G0003 on air_mos.pix

Collecting GCPs from a PIX/text file

Ground control points collected with a GPS will often be delivered in a text file.

Each point will have X, Y, E and possibly Point Id information. Before you collect

ground control points in the field, you need to ensure that you can clearly see that

location in the raw image. The pixel and line coordinates for the uncorrected image

must be determined manually and transferred to the GCP Collection window.

To import GCPs from a file:

1. From the Ground control source list in the GCP Collection window, select PIX/Text file.

The Read GCP From PIX/Text File window opens.

2. Click Select.

3. From the AIRPHOTO folder, select S129.GCP and click Open.

4. From the Sample formats list, select IXYE.

IXYE format The format of the S129.GCP file is IXYE, which means that each row of text contains the following information from left to right:

• The GCP ID (I)

• The georeferenced East/West coordinate (X)

• The georeferenced North/South coordinate (Y)

• The elevation (E)

The Pixel and Line positions will be taken from the uncorrected S129 photo.

5. Click Apply Format.

The information for each point is listed in the GCPs extracted from file area.

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Figure 2-8: Read GCP From Text File window with GCPs in IXYE format

6. Click OK.

The GCP Text file window opens.

Figure 2-9: GCP Text File window

To transfer the coordinates:

1. In the GCP Text File window, select G0014 and click Transfer to GCP collection panel.

The IXYE coordinate is transferred to the GCP Collection window. If Auto locate is enabled in the GCP Collection window, OrthoEngine will estimate the position of the GCP in the uncorrected image. In the viewer for S129, you will notice that the crosshairs have automatically moved near the correct position for G0014.

2. Use the figure below to more accurately position the crosshairs for G0014.

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Figure 2-10: G0014 on S129.pix

3. Adjust the position of the crosshairs and click Use Point.


2665 Pixel

2392 Line



5. In the GCP Text File window, select G0015 and click Transfer to GCP collection panel.

6. Use the figure below to more accurately position the crosshairs for G0015.

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Figure 2-11: G0015 on S129.pix

7. Adjust the position of the crosshairs and click Use Point.


4131 Pixel

4159 Line



Tip


To save time, the GCPs for S130.pix, S188.pix and S189.pix will be imported from a text file in IPLXYE format.

IPLXYE format The format of the S130.GCP file is IPLXYE, which means that each row of text contains the following information from left to right:

• The GCP ID (I)

• The image pixel position (P)

• The image line position (L)

• The georeferenced East/West coordinate (X)

• The georeferenced North/South coordinate (Y)

• The elevation (E)

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To open S130.pix as the Working Image:

1. On the GCP/TP Collection toolbar, click Open a new or existing image.


2. Select S130 and click Quick Open & Close.

The Open Image window closes and a viewer opens containing the S130 photo as the Working Image.

Note

One image is always the Working image, while the other image is the Reference image. Click Reference on the viewer toolbar to set an image to Working.

To import the GCPs for S130.pix:

1. On the GCP Collection window, click Select PIX/Text File.

The Read GCP from Text File window opens.

2. Click Select.

3. From the AIRPHOTO folder, select S130.GCP and click Open.

4. From the Sample formats list, select IPLXYE.

5. Click Apply Format.

The information for each point is listed in the GCPs extracted from file area.

Figure 2-12: Read GCP From Text File window for GCPs in IPLXYE format

6. Check that the GCPs are listed correctly and click OK.

After the GCPs are read in, the GCP ID number for each point appears in the viewer in red. Check to see that the GCP positions are correct.

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To import the GCPs for S188.pix and S189.pix:

1. Open S188.pix and S189.pix.

2. Make S188 the Working image.

3. Import the GCPs for S188 using the S188.GCP text file.

4. Make S189 the Working image.

5. Import the GCPs for S189 using the S189.GCP text file.

Note

When a photo is selected as the Working image, the GCPs collected for this image are listed in the GCP Collection window.

Collecting stereo GCPs

A stereo ground control point (GCP) is a cross between a regular GCP and a tie

point. It is a feature with known ground coordinates that you can clearly identify in

two or more images. They have the same Point ID, Easting and Northing

coordinates and elevation value, but the pixel and line location is different in each

image.

Therefore, a stereo GCP not only determines the relationship between the raw

images and the ground, like a GCP, but also identifies how the images in your

project relate to each other, like a tie point. The result is a stronger math model

since the stereo GCPs add redundancy and are weighted more heavily in the

calculation of the math model.

To collect Stereo GCPs:

1. Ensure photos S129 and S130 are open, as well as the GCP Collection window.

2. Make S130.pix the Working image.

3. In the GCP Collection window, locate and select Point ID G0016.

This loads the GCP image and georeferenced position information based on the S130.pix photo. If Auto Locate is selected, this GCP is loaded in each of the viewing windows.

4. Make the S129.pix photo the Working image by clicking Reference on the viewer toolbar.

A list of GCPs collected for the Working image now appears in the GCP Collection window. The identification of the desired stereo point, G0016, is listed in the GCP Collection window for S129. The georeferenced position information for this point is shown in the window. However, the image pixel and line information for the Working image is not listed, since you have not yet associated the point G0016 with S129.pix.

5. Place your cursor on the feature that corresponds with point G0016 on S129.

6. Click Use Point.

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Note that the Image pixel and line information updates in the GCP Collection window.

7. Click Accept.

This point is now registered to the same georeferenced location on the earth for both overlapping images, making it a Stereo GCP.

8. Collect another stereo GCP based on Point ID G0003.

Using Compute model

The Compute model feature appears on the GCP Collection windows and the Tie

Point Collection window when you are creating a project using a rigorous model.

When you select Compute model, OrthoEngine calculates the math model every

time you add a point to the project. This can help you determine whether the point

that you collected is good enough for your project.

To enable Compute model:

• At the top of the GCP Collection window, enable the Compute model option.

Residual errors are calculated for each GCP and are listed in the Residual column.

Residual errors are the difference between the coordinates that you entered for the ground control points (GCPs) or tie points and where those points are according to the computed math model. Computing the math model will be discussed further in Lesson 2.3.

Check points

Whereas GCPs are used in computing the math model, Check Points are used to

check the accuracy of the math model and are not used to compute the math

model. OrthoEngine calculates the difference between their position and the

position determined by the model; therefore, the Check Points provide an

independent accuracy assessment of the math model.

Next, you will take an existing GCP and turn it into a Check Point.

To create a Check Point:


2. In the GCP Collection window, select G0014 or a GCP with a high residual value.

3. From the list beside the Point ID box, select Check.

4. Click Accept.

This point is now a Check point and is not included in the bundle adjustment.

To convert a Check Point to a GCP:

1. In the GCP Collection window, select G0014.

2. From the list beside the Point ID box, select GCP.

3. Click Accept.

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This point is once again a GCP and is included in the bundle adjustment.

4. Click Close.

Tip


In this lesson you:

• Collected GCPs from a geocoded image

• Imported GCPs from a text file

• Collected stereo GCPs

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• Collect tie points manually

• Collect tie points automatically

• Check the layout of the images

To add tie points, you require:

• The project file airphoto.prj from Lesson 2.1 for photos S129.pix, S130.pix, S188.pix and S189.pix.

The objective of this lesson is to tie the four photos together and check the layout

to ensure a proper distribution of points.

Term

A tie point is a feature that you can clearly identify in two or more images that you can select as a reference point.

Tie points do not have known ground coordinates, but you can use them to extend

ground control over areas where you do not have ground control points (GCPs).

Used in rigorous models such as Aerial Photography and Satellite Orbital (high and

low resolution) math models, tie points identify how the images in your project

relate to each other. In a project using the Rational Functions math model where

you have imported the polynomial coefficients distributed with the data, you can

collect tie points and ground control points to compute a transformation to improve

the fit between the images.

For projects using the Aerial Photography math model, you usually collect tie points

in a three-by-three pattern over the image. Since the images have a 60 percent

overlap between each other and a 20 percent overlap between the strips, you can

use the three-by-three pattern to connect six overlapping images.

Projects using the Satellite Orbital math model generally have fewer images so you

can collect tie points wherever overlap occurs. Since the overlap between satellite

images is unpredictable, satellite imagery generally covers a large area containing

a lot of ground control.

Using the tie points in the calculation of the math model ensures the best fit not only

for the individual images, but for all the images united as a whole. Therefore, the

images will fit the ground coordinate system, and overlapping images will fit each

other.

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Selecting good TPs

• Select features that can be identified accurately at the resolution of the raw image.

• Select features that are close to the ground. Because elevated features in the image will appear to “lean”, selecting features on the ground will ensure that the point is not displaced from the actual ground coordinate.

• Avoid picking shadows. These are easy to see in the image, but they are not permanent features and can move from one image to another.

• Avoid repetitive features such as parking lots and lines on a highway, since it is easy to select the wrong one.

• While tie points that join two images together are effective, tie points that join 3 or more together are even better. Tie points that join multiple images together produce a more accurate model.

• If the elevation value at the tie point location is known, then enter that value in the elevation field in the tie point collection panel. These points help to quantify elevation, improving the accuracy of the geometric model.

Collecting tie points manually

If you have several images open, you will notice that one image resides in a viewer

labeled Working while the others are labeled Reference. The Tie Point Collection

window collects and displays the tie points from the image in the Working viewer

only. Click the Reference button to switch the viewer to Working. You can collect

the same tie point in each image by clicking Reference in a viewer, collecting the

tie point, and then repeating the process for each image.

Notice that the block of photos have an area in common. This area represents the overlap. The figure below depicts the four photos with the area of overlap bounded by a black rectangle.

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Figure 2-13: Overlap

To collect tie points manually:

1. On the GCP/TP Collection toolbar, click Manually collect tie points.

The Tie Point Collection window opens.

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Figure 2-14: Tie Point Collection window

2. In the Auxiliary Information section, click Select.


The DEM File window opens where you select the channel containing the DEM information. This will be your source of elevation for your tie points.

Entering tie point elevation is optional. You can either load a DEM or select the Elevation option and manually enter the elevation. The elevation of the tie point is automatically incorporated into the math model.

4. Enter a Background elevation of -150 and click OK.


6. Find a location in S129.pix that can be clearly seen in the overlap area in S130.pix, zooming in as necessary.

7. Click to place the cursor at this location.

A red crosshair appears.

8. In the S129 viewer, click Use Point.

If Auto locate is enabled in the Tie Point Collection window, OrthoEngine estimates the position of the Tie Point in the overlap area of the other photos. You need to refine the position of the crosshairs on S130.pix before accepting the tie point on this image.

9. Place the cursor at the exact same location in S130.pix, zooming in as necessary.

10. In the S130 viewer, click Use Point.

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11. In the Tie Point Collection window, click Accept.

The point is listed in the Accepted Tie Points table.

12. Repeat these steps to collect at least four additional tie points for the project.

For Auto locate to work, you need to click Use Point on the Working image after placing the crosshairs on a feature that can be seen in both images. It does not matter which image you set as the Working image.

Tie points can also be collected between flight lines.

13. Click Close.

Tip


Collecting tie points automatically

Since tie points are simply matching points in two or more images, OrthoEngine

can automate the tie point collection by using image correlation techniques. Image

correlation uses a hierarchical approach to find matching features in the

overlapping area between two or more images using moving frame with a search

radius of 100 pixels by default.

The first attempt at correlation is performed on very coarse versions of the images.

Depending on the resolution of the images or the accuracy of the math model, the

predictability of the match can be greater than the size of the search frame. For

example, if your image is 0.10 meter resolution and your estimated math model is

off by 15 meters, then the features that you are trying to match are 150 pixels away

from their estimated locations.

Therefore, if you are experiencing a low success rate with the Automatic Tie Point

Collection, increasing the search radius may improve the results Increasing the

search radius, however, will increase the processing time.

The matching process can be accomplished if your project meets one or more of

the following criteria:

• The exterior orientation of each image was computed based on ground control points (GCPs) and/or tie points.

• You collected three tie points between every pair of overlapping images.

• You used a Global Positioning System (GPS) to obtain the x, y, and z coordinates for each image center, and you estimated the omega, phi, and kappa rotations, or they were supplied by an Inertial Navigation System (INS).

• You used the Satellite Orbital math model and imported the ephemeris or orbit information with the satellite data.

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• You imported the polynomial coefficients from the satellite data for use with the Rational Functions math model.

Now that you have collected some tie points manually, you will collect tie points automatically.

To open the Automatic Tie Point Collection window:

• On the GCP/TP Collection toolbar, click Automatically collect tie points.

The Automatic Tie Point Collection window opens.

Figure 2-15: Automatic Tie Point Collection window

Tie point distribution pattern

There are two options for the distribution pattern:

Entire image: To distribute the tie points evenly over the entire image and match each tie point in all the overlapping images. This is normally used to generate standard tie point distributions for aerial photographs such as the three-by-three pattern.

Overlap area: To distribute the points evenly only in the overlap area between any pair of overlapping images. This is normally used for satellite images or for aerial photographs with less than 60% overlap.

Tie point options

The Tie Point Options section contains the following four options:

Tie points per area: You specify the number of Tie Points to generate per overlap area for each pair of images.

Matching threshold: This is a minimum correlation score between points that will be considered a successful match. This aid in controlling the quality of the automatically collected tie points. The range is from 0 to 1, with a default value of 0.75. Increasing the threshold may reduce the number of tie points accepted.

Search radius: This is the number of pixels defining the radius of the search frame. If you are experiencing a low success rate with the Automatic Tie Point Collection, increasing the search radius may improve the results.

Approximate Elevation: Entering an approximate elevation allows the matching algorithm to make a better first estimate of the parallax between the images. This improves the success rate of the collection process.

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Images to process

Tie Points are automatically collected in one of two ways:

All Images: To collect tie points for all images in the project.

Working Image: To collect tie points for the image designated as the working image. This option is available if there is currently a Working Image in the viewer.

Processing start time

Two options are available for the start time:

Start now: Begins the process after you click the Collect Tie Points button.

Start at (hh:mm): To run the process overnight, select the Start at (hh:mm) option, which lets you start the process at any time within the next 24 hours.

To set up and run Automatic Tie Point collection:

1. For the Distribution Pattern, use the default setting of Entire image.

2. For the Tie points per area, enter the value 3.

3. For the Matching threshold, use the default value of 0.75.

4. For the Search radius, use the default value of 100 Pixels.

5. Enter an Approx. elevation value of 200 m.

This value is the average elevation of the terrain in your project.

6. For Image to Process, use the default option of All images.

7. Click Collect Tie Points.

A progress bar appears at the bottom of the window to monitor the status. After the process is complete, a message box opens indicating the total number of tie points found.

8. Click Close.

Verifying automatic tie pointsAutomatic tie points should always be verified to ensure that a given point was collected over the same feature on your imagery. This is especially important if there are clouds or snow in the imagery as the image correlation technique used for the TP collection process sometimes fails in these regions. Automatically collected tie points are given an ID with the prefix A.

To verify the automatic tie points:

1. On the GCP/TP Collection toolbar, click Manually collect tie points.

The Tie Point Collection window opens.

2. Open all four photos.

3. Activate the Auto Locate option.

4. In the Tie Point Collection window, select a tie point with the prefix A.

The viewers update to display the imagery at 1:1 resolution centered on the selected TP.

5. If you are not satisfied with the auto tie point, click Delete.

6. Verify the remaining automatic tie points and click Close.

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Checking the layout of the images

The Image Layout feature is a quality control tool that reveals the relative

positioning of the image footprints and displays a plot of the distribution of the

ground control points (GCPs) and tie points for the entire project. Images in the

project are represented by a frame with crosshairs and ID at the center. If

information is insufficient to position the images relative to the ground, a message

will appear in the status bar to indicate that you need to collect more GCPs.

To open Image Layout:

1. On the GCP/TP Collection toolbar, click Display overall image layout.

The Image Layout window opens. The Overview area shows the center of each image in the project. The top of the window points northward.

2. Under Overview, click a crosshair to reveal the image’s footprint.

In the right side of the window, the selected image is displayed with a red frame, while the other images are framed in blue. The GCPs are displayed as red squares, while the TPs are displayed as blue squares.

3. To open an image, double-click the image footprint or click Quick Open.

If you are not satisfied with the distribution, edit your GCPs and tie points.

4. Click Close.

Figure 2-16: Image Layout window showing GCPs and TPs

Tip


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In this lesson you:

• Collected tie points manually

• Collected tie points automatically

• Checked the image layout

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• Perform the model calculation

• Examine vector residual plots

• Read the residual report

To calculate the math model, you require:

• The project file airphoto.prj from Lesson 2.2 for photos S129.pix, S130.pix, S188.pix and S189.pix.

Understanding rigorous math models

The computation of a rigorous math model is often referred to as a bundle

adjustment. The math model solution calculates the position and orientation of the

sensor—the aerial camera or satellite—at the time when the image was taken.

Once the position and orientation of the sensor is identified, it can be used to

accurately account for known distortions in the image. When the model is

calculated, the image is not manipulated. OrthoEngine simply calculates the

position and orientation of the sensor at the time when the image was taken.

In the Aerial Photography math model, the geometry of the camera is described by

six independent parameters, called the elements of exterior orientation. The three-

dimensional coordinates x, y, and z of the exposure station in a ground coordinate

system identify the space position of the aerial camera. The z-coordinate is the

flying height above the datum, not above the ground. The angular orientation of the

camera is described by three rotation angles: Omega, Phi, and Kappa.

In the Satellite Orbital math model, the position and orientation of the satellite is

described by a combination of several variables of the viewing geometry reflecting

the effects due to the platform position, velocity, sensor orientation, integration

time, and field of view. The ephemeris data will allow a bundle adjustment to be

computed immediately with or without GCPs or tie points.

During the math model calculation, OrthoEngine uses ground control points

(GCPs) and tie points combined with the knowledge of the rigorous geometry of the

sensor to calculate the best fit for all images in the project simultaneously.

For Aerial Photography projects, the model calculation can only be performed after

you collect the minimum number of ground control points and tie points. If you are

using data from the Global Positioning System (GPS) with or without Inertial

Navigation System (INS) data, the math model calculation can be performed

immediately. Due to the ephemeris data, the math model calculation for Satellite

Orbital projects is performed immediately with or without GCPs or tie points.

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You can add GCPs and tie points to refine the math model's solution. Not all the

GCPs in your project will have the same reliability. When the math model

calculation is performed, the GCPs, tie points, GPS data, and INS data will be

automatically weighted inversely to their estimated error. The most accurate GCPs

or tie points should affect the solution the most, and the least reliable should affect

the solution the least. Using many GCPs and tie points provides redundancy in the

observations so that a few bad points will not greatly affect your model, and the bad

points will be easier to identify.

Once the sensor orientation is calculated, it is used to drive all the other processes

such as digital elevation model extraction, editing in three-dimensional stereo, and

orthorectification. You must obtain an accurate math model solution before

continuing with other processes.

Computing the model

There are two ways to calculate the math model:

• You can do an update after each GCP or TP is collected

• You can calculate the model after all GCPs and TPs are collected

The Compute model feature appears on the GCP Collection and the Tie Point

Collection window when you are creating a project using a rigorous model. When

you select Compute model, OrthoEngine calculates the math model every time you

add a point to the project. This can help you determine whether the point that you

collected is good enough for your project.

To enable Compute model:

• At the top of the GCP Collection or Tie Point Collection window, enable the Compute model option.

Note

If Compute model is disabled, the GCPs or TPs are listed with a status of Stale in the Residual column of the corresponding Accepted Points table.

The second way to calculate the math model is found on the Model Calculations toolbar on the OrthoEngine window.

To compute the rigorous math model:

1. On the OrthoEngine window in the Processing steps list, select Model Calculations.

A new toolbar with one icon appears.

2. Click Compute model.

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Troubleshooting the math model solution

Since determining the best possible solution for the math model is the foundation

of your project, it is important for you to know if your solution is good enough to

achieve the results you expect. If it is not, you must also know what to do to adjust

the model.

The Residual Errors will help you determine if the solution is good enough for your

project. Residual errors are the difference between the coordinates that you

entered for the ground control points (GCPs) or tie points and where those points

are according to the computed math model. You can see the residual errors for the

image on the GCP Collection windows in the Residual column or you can generate

a Residual Report for the entire project.

Residual errors do not necessarily reflect errors in the GCPs or tie points, but rather

the overall quality of the math model. In other words, residual errors are not

necessarily mistakes that need to be corrected. They may indicate bad points, but

generally, they simply indicate how well the computed math model fits the ground

control system.

Note

In Rational Functions computed from GCPs, Polynomial, and Thin Plate Spline projects, images are not connected together with tie points. Therefore, the math model and the resulting residual errors are calculated for each image separately. If you selected the Thin Plate Spline math model for your project, the residual errors will always indicate zero. Use Check Points to check its accuracy.

Another way to verify the quality of the model is to collect some GCPs as Check

Points. Check Points are not used to compute the math model, but OrthoEngine

calculates the difference between their position and the position determined by the

model and includes the error in the Residual Errors report. Therefore, the Check

Points provide an independent accuracy assessment of the math model.

In most projects you should aim for the residual errors to be one pixel or less.

However, you should also consider how the resolution of the image, the accuracy

of your ground control source, and the compatibility between your ground control

source and the images can affect the residual errors.

• You may want to use a topographic map as a ground control source, however, features on topographic maps may be shifted several meters for aesthetic reasons. This limits the accuracy of the coordinates that you can obtain from the map. Also, the detail visible on a 1:50,000 scale topographic map may not be compatible with the high resolution of an aerial photograph. For example, if you choose a road intersection in a topographic map as your coordinate, the same road intersection in the aerial photograph may consist of several pixels. Therefore, the residual error will likely be larger than a pixel.

• An existing LANDSAT orthorectified image may make a convenient ground control source for registering a new IKONOS image, but the resolution of the

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LANDSAT image is 30 meters and the resolution of the IKONOS raw image is 1 meter. Therefore, even if you could pick the right pixel in the IKONOS image, your GCP from the LANDSAT image is only accurate to 30 meters. You cannot achieve accuracy of 2 to 4 meters unless your ground control source is equally accurate.

• At first glance, a residual error of 250 meters in ground distance may appear too high. However, if your raw data has a resolution of 1000 meters, such as AVHRR, you have already achieved sub-pixel accuracy.

Identifying errors in the model

Although residual errors are not necessarily mistakes that need to be corrected,

they may indicate problems with the math model. The following conditions may

help you to identify such problems.

Outliers A ground control point (GCP) or tie point with a very high residual error compared

to the others in the Residual Errors report may indicate an error in the original GCP

coordinate, a typographical mistake, or an error in the position of the GCP or tie

point on the raw image. These points are called outliers.

To correct an outlier:

• Verify that the feature you picked in the raw image corresponds to the one from your source.

• Verify that the typed ground coordinate matches the coordinate listed in your source.

• Confirm that the ground coordinate you collected in the raw image is consistent with the coordinate you selected from the vector or the geocoded image.

• Verify that the projection and datum for the ground coordinate are correct.

If all else fails, delete the point or change it to a Check Point.

Poor math model solution

If the residual errors for all the GCPs and/or tie points in general are high, it may

indicate a poor model solution. Poor model solutions can be the result of inaccurate

GCPs, errors in the projection or datum, inadequate distribution of the ground

control, or insufficient ground control.

Residual errors are all zero

If all the residual errors for the GCPs and tie points read zero, it usually indicates

that you have collected only the minimum number of ground control points or fewer.

Collect more GCPs and tie points.

However, if you selected the Thin Plate Spline math model for your project, the

residual errors will always indicate zero. Use Check Points to check the accuracy

for the Thin Plate Spline math model.

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Systematic trends in residual errors

If you have high residual errors in one part of an image or project, it can indicate

that you need more ground control in the problem area, or it may indicate that you

have one or more bad points in the area that are skewing the math model. Some

bad points are difficult to identify since some points may compensate for others.

Vector residual plots

You can view a visual representation of the residual errors by superimposing

vectors of the collected points and residual errors over the image. This allows for

visual analysis and quality assurance of the math model. Observing the patterns in

the vectors can help you identify possible causes and solutions for the errors.

For example:

• If you collected a point on the southeast sidewalk corner instead of the northwest corner, the vector would point to the position calculated by the model.

• If all the vectors are pointing in the same direction and magnitude, it may indicate a possible datum shift.

• If the vectors point in one direction on the east to west flight lines and point in the opposite direction in the west to east flight lines, it means that you may have collected the fiducials incorrectly.

To open the Display Residuals window:

• From the Options menu on the OrthoEngine window, select Residual Display.

The Display Residuals window opens.

Figure 2-17: Display Residuals window

Residuals list The residuals can be plotted using one of three formats:

X,Y: to display the x-axis and y-axis residual errors for each point as separate vectors.

XY: to display the residual error for each point as one vector representing the combined x-axis and y-axis residual errors.

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Z: (for stereo GCPs and stereo check points only) to display the residual error for the elevation of each point as one vector along the y-axis.

Coordinate system

There are two options for the axis on which the residual error will be displayed.

Ground: to display the residual error using Easting and Northing directions (ground coordinate system).

Image: to display the residual error using the x and y axis directions (image coordinate system).

To open a photo:

1. From the Processing step list, select GCP/TP Collection.

2. Click Open a new or existing image.

3. Select S129.pix and click Quick Open and Close.

4. On the GCP/TP Collection toolbar, click Collect GCPs Manually.

The GCP Collection window opens.

You will now plot the residuals on the photo you opened.

To plot the residuals on a photo:

1. In the Display Residuals window, use the default of X,Y for the Residuals option.

2. For the Coordinate system, select Image.

3. Under the Display column, click beside GCPs, Check Points and Tie Points.

A red check mark appears in each cell.

4. Click Apply.

To view the vector residuals:

1. In the GCP Collection window, select G0001 from the Point ID list.

The viewer is updated to show this point at a resolution of 1:1. The vector residuals are plotted as red lines.

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Figure 2-18: X,Y Residual plot for G0001 on S129

2. On the Display Residuals window, change the Coordinate system to Ground and click Apply.

The Residuals are displayed using the ground coordinate system instead of the image coordinate system.

3. Examine how the residuals are plotted when you select the NE and H options.

Note

By default, the magnification factor for the residual plots is set to 5. The magnification factor exaggerates the appearance of the residual error so it becomes easier to observe systematic patterns. If the residual error is one pixel and the Magnify value is five, the residual error is displayed as five pixels long in the viewer.

To highlight points with high residual values:

1. Change the Coordinate system back to Image.

2. For the Residuals option, select X,Y.

3. In the Highlight residual greater than box, enter 1 pixels.

4. Click Apply.

5. In the GCP Collection window, select a GCP that has a Res X or Res Y residual greater than 1.

Residual errors over the 1 pixel threshold are highlighted by an increase in the thickness of the vector lines.

6. Click OK.

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Reading the Residual Report

The Residual Report helps you determine if the math model solution is good

enough for your project.

To open the Residual Errors window:

1. On the GCP/TP Collection toolbar, select Residual report.

The Residual Errors window opens.

The Residual report can also be opened by selecting Reports from the Processing Step list.

Figure 2-19: Residual Errors window

Residual units Defines whether residual errors are reported in Ground units or Image pixels.

Show points Lets you select which types of points to display.

Show in All images: To display all the images in the project.

Selected image: To display one image in the project, click an image in the table under Image ID and click Selected image, or you can type the image's identification in the Selected image ID box and press ENTER.

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Sort by Residual: To order the residual errors from the highest to the lowest value.

Data snooping: To order the normalized residual errors from highest to lowest probability of error that is not noise. Because the residuals are highly correlated to one another, a blunder in one point may cause all points to have higher residuals. Because of this, it may be difficult to isolate the source of the problem. Data snooping tries to uncorrelate the errors in order to isolate the bad point by calculating this statistical value.

Residual errors For each point in the project, the Residual Errors window lists the information

shown in the following table.

Caution

If you make any edits to your model in the Residual Errors window, make sure you recalculate the model by clicking Compute Model.

Tip

This would be a good time to save your project file.

In this lesson you:

• Performed the model calculation

• Examined vector residual plots

• Read the residual report

Table 2.1: Residual Errors

Heading Description

Point ID The point’s identification number

Res The combined residual error

Res X The point’s X-axis residual error

Res Y The point’s Y-axis residual error

Type Type of point, GCP or TP

Image ID Image to which the point belongs

Image X X-coordinate - # of pixels from left

Image Y Y-coordinate - # of pixels from top

Comp X Computer adjusted X-coordinate

Comp Y Computer adjusted Y-coordinate

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Checkpoint

Module 3 After the preparation stage, you may need to extract a Digital Elevation Model or

build a DEM from existing data. If so, proceed to the data extraction stage which

begins with Module 3: DEM operations. Here, you learn how to extract a DEM from

four airphoto scenes. However, the procedures can also be used to extract a DEM

from any stereo images.

Module 4 If you have an existing raster DEM to use for orthorectification, go directly to the

correction stage, which begins with Module 4: Orthorectification.

In Module 4, you learn how to set up, select your DEM, and then orthorectify your

satellite images or aerial photographs.

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DEM operations


Lesson 3.1 Creating epipolar images

Lesson 3.2 Extracting and geocoding the DEM

Lesson 3.3 Building a DEM

Data extraction stage

The Data Extraction stage consists of the following module:

• Module 3: DEM operations

In Module 3, you learn how to extract a Digital Elevation Model (DEM) from four

airphoto scenes.

OrthoEngine needs a raster DEM to create an orthorectified image or photo.

The objectives of this module are two-fold:

• To extract and geocode a DEM from pairs of stereo images

• To build a raster DEM from contour data

Module

3

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Geomatica OrthoEngine - Module3: DEM operations



• Set up for epipolar image creation

• Create epipolar images in batch mode

The objective of this lesson is to set up and convert four airphoto scenes into their

epipolar projections. OrthoEngine is able to create epipolar images for multiple

stereopairs in batch mode. You are able to add your stereopairs to a list and

process them in one step.

To complete this lesson you require:

• The airphoto scenes S129.pix, S130.pix, S188.pix, and S189.pix.

• The project file airphoto_model.prj, which contains the four airphotos from the Richmond Hill dataset. This project contains all the required GCPs and tie points and an up-to-date model.

Epipolar images are stereo pairs that are reprojected so that the left and right

images have a common orientation, and matching features between the images

appear along a common x axis. Using epipolar images increases the speed of the

correlation process and reduces the possibility of incorrect matches.

Figure 3-1: Comparing raw images to epipolar images

Epipolar images are used in both extracting DEMs from stereo images and for three-dimensional stereo editing.

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Creating epipolar images

To open the Create Epipolar Images window:

1. On the OrthoEngine window in the Processing steps list, select DEM From Stereo.

A new toolbar with five icons appears.

Figure 3-2: DEM From Stereo toolbar

2. On the DEM From Stereo toolbar, click Create Epipolar Image.

The Generate Epipolar Images window opens.

Figure 3-3: Create Epipolar Image window

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You need to select the scenes to be used for the generation of the epipolar images.

Left image This area displays the candidates for the left-looking image of the stereo pair. All four airphotos are listed in the Left Image area when the window opens.

Right image The Right Image area lists the candidates for the right-looking image.

Because this project contains airphotos, the idea of Left and Right Images is not important for DEM extraction. You simply need to select the left and right images in order to create pairs of epipolar images. If you decide to proceed to 3-D feature extraction, which also uses epipolar pairs, you will save time if you generate pairs that can be use in 3-D viewing. If your photos are scanned north up, the photo that is geographically on the left is the left image, and the photo that is geographically on the right is the right image.

To create the epipolar images:

1. From the Epipolar Selection list, select Maximum overlapping pairs.

2. For the Minimum Percentage Overlap, enter 60.

This will automatically select pairs with a minimum of 60 percent overlap.

3. Click Add Epipolar Pairs To Table.

The List of Epipolar Pairs lists two sets of epipolar pairs: S129 with S130 and S188 with S189.

Figure 3-4: Completed List of Epipolar Pairs

4. In the Options section, specify the Working Cache.

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Note

It is recommended that you NOT exceed 50% of the available RAM. Specifying more than half may significantly reduce performance since the operating system needs RAM for its own operations.

5. Select a Down sampling factor of 2.

This is the number of image pixels and lines that will be used to calculate one epipolar image pixel. For example, typing 2 means that two adjoining pixels and two adjoining lines will form one pixel in the epipolar image. The spatial detail of your resulting epipolar images will not be as high as your original imagery. You can adjust this value if you see noisy features in your image that you do not want to see in your DEM.

6. For the Down sample filter, select Average.

7. Click Save Setup.

Save Setup saves the options chosen for batch processing with Automatic DEM Extraction.

Generate Pairs begins the process based on the time set under Processing Start Time. Use this option if you are using the epipolar pairs for 3-D Feature Extraction. If you are using the epipolar pairs for Automatic DEM Extraction, you can either use this option or Save Setup.

8. Click Close.

Tip

This would be a good time to save your project file.

Now that you have setup to generate the epipolar pairs, you will set up for DEM extraction in the next lesson. The epipolar pairs and the DEM will be generated all at once.

In this lesson you:

• Set up for epipolar image creation

• Created epipolar images in batch mode

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• Set up to extract and geocode a DEM

• Extract and geocode the DEM

• Edit the DEM

This lesson describes how to extract, geocode, and edit a DEM. To complete this

lesson you require the airphoto epipolar pairs generated in the previous lesson.

To complete this lesson you require:

• The airphoto scenes S129.pix, S130.pix, S188.pix, and S189.pix.


A digital elevation model (DEM) is a digital file of terrain elevations for ground

positions. It is a raster layer representing the elevation of the ground and objects,

such as buildings and trees, with pixel values in the images.

You can extract a digital elevation model (DEM) from stereo pairs of images, which

are two or more images of the same area taken from different view points. This

method can be very useful for creating a DEM for inaccessible areas. You can

obtain stereo pairs from aerial photographs, digital or video images, and these

sensors: ASAR, ASTER, IRS, IKONOS, SPOT, QUICKBIRD, RADARSAT and

WorldView-1.

OrthoEngine uses image correlation to extract matching pixels in the two images

and then uses the sensor geometry from the computed math model to calculate x,

y, and z positions.

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Figure 3-5: Creating a DEM from stereo images

Setting up to extract and geocode a DEM

The process of extracting a digital elevation model (DEM) from stereo images

consists of three steps:

• Convert the raw images into epipolar pairs.

Epipolar images are stereo pairs that are reprojected so that the left and

right images have a common orientation, and matching features between

the images appear along a common x axis.

• Extract DEMs from the overlap between the epipolar pairs.

The resulting DEMs are called epipolar DEMs. They are not

georeferenced at this stage.

• Geocode the epipolar DEMs and stitch them together to form one DEM.

The result is one DEM reprojected to the ground coordinate system.

Editing the epipolar DEMs

If you want to edit the DEM before it is geocoded, do not select Create Geocoded

DEM. The DEM extraction will produce a file that contains the epipolar pair in the

first channel, the correlation score (if selected) in the second channel, and the

corresponding epipolar DEM in the third channel.

After you generate the epipolar DEMs, you can edit them, geocode them, and then

integrate them into one DEM.

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Editing the geocoded DEM

When you use the Automatic DEM Extraction window to complete the entire

process in one operation, OrthoEngine builds a model based on all the selected

epipolar pairs and uses that model when the DEMs are geocoded. The geocoded

DEMs are automatically stitched together and saved in a file. Because

OrthoEngine uses a model to process all the epipolar pairs, the resulting integrated

geocoded DEM is slightly more accurate than if you completed the process

manually.

You can edit the geocoded DEM, however, the file will not include the epipolar

pairs. If you selected Create Score Channel, the correlation score is saved as the

first channel in the file and the geocoded DEM as the second.

To open the Automatic DEM Extraction window:

• On the DEM From Stereo toolbar, click Extract DEM automatically.

The Automatic DEM Extraction window opens.

Figure 3-6: Automatic DEM Extraction window

Stereo pair selection

The epipolar pairs you set up in the last lesson are listed in the Stereo Pair Selection table.

To select the stereo pairs:

• In the Select column, click to select both stereo pairs.

You can also click Select All to select all pairs that appear in the list.

If the epipolar pairs do not exist or are not available, OrthoEngine will automatically generate the epipolar pairs using the options that you saved in the Generate Epipolar Images window.

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Epipolar DEMs The Epipolar DEM column specifies the output name for the extracted DEMs. Two epipolar DEMs will be created from the two sets of epipolar pairs.

DEM report A text report is generated during the DEM extraction process. The report indicates the parameters used to extract the DEM as well as the correlation success. The names of the output DEM reports are specified in the DEM Report column.

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Extraction options

The Extraction Options section contains the following options that govern the quality and resolution of the DEM extraction:

Minimum elevation and Maximum elevation: The minimum and maximum elevations are used to estimate the search area for the correlation. This increases the speed of the correlation and reduces errors. If the resulting DEM contains failed values on peaks or valleys, increase the range.

Failure value: This value is assigned to failed pixels within the extracted DEM. Specifying a value assists the manual editing process. The default Failure Value is -100.

Background value: This value is used to represent “No Data” pixels in the DEM. The "No Data" or background identifies the pixels that lie outside the extracted DEM overlap area so they are not mistaken for elevation values. For DEMs generated by OrthoEngine, the background elevation defaults to -150.

DEM detail: DEM Detail determines how precisely you want to represent the terrain in the DEM. Selecting High, Medium or Low determines at which point in correlation process you want to stop. Low means that the process stops during the coarse correlation phase on aggregated pixels so the level of detail in the DEM will be quite low. High means the process continues until correlation is performed on images at full resolution.

Output DEM channel type: This option allows you to save the DEM in either a 16-bit signed channel or 32-bit real channel.

Pixel sampling interval: The Pixel Sampling Interval sets the frequency of samples taken in the processing of the DEM extraction. It also controls the size of the pixel in the final DEM relative to the input images. The higher the number you choose, the larger the DEM pixel will be, and the faster the DEM is processed.

Use clip region: This option allows you to process only the area determined in the Define Clip Region window, which results in smaller DEMs and faster processing.

Fill holes and filter: This option will enhance the output quality of the DEM by interpolating the failed areas and filtering the elevation values automatically.

Create score channel: This option generates an additional image channel to represent the correlation score for each DEM pixel. The correlation score will help you identify pixels where correlation was weak or failed, which gives you a better impression of the success of the operation.

Delete Epipolar Pairs after use: This option deletes the epipolar pairs from the disk to save space after the DEM is generated.

To set the extraction and geocoding options:

1. For the Minimum elevation, enter 150.

2. For the Maximum elevation, enter 250.

3. In the DEM detail list, select Medium.

4. Set the Pixel sampling interval to 2.

Every second pixel is sampled and processing time is reduced.

5. Select the Fill holes and filter option.

6. Select the Create score channel option.

7. Select the option to Create Geocoded DEM.

This will geocode and merge the extract epipolar DEMs in one step.

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8. For the Output File name, enter GeocodedDEM.pix.

9. For the DEM Bounds, select All Images.

This will use the extent of all the images in the Stereo pairs table as the extents for the DEM.

10. For the Output Options, select Highest Score.

Output options As the epipolar DEMs are extracted and geocoded, they are added to the geocoded DEM file. When a new geocoded DEM is added to the file and it overlaps an existing geocoded DEM, you must choose a method to determine which pixel value will be used. There are three methods:

Use last value: To replace the pixel values in the overlap area in the existing geocoded DEM by the pixel values of the geocoded DEM being added to the file.

Average: To replace the pixel values in the overlap area by the average pixel values between the existing geocoded DEM and the one being added to the file.

Highest Score: To replace the pixel values in the overlap area by the pixel value with the highest correlation score between the existing geocoded DEM and the one being added to the file. This option is only useful if you select Create Score Channel.

Figure 3-7: Automatic DEM Extraction after the set up is complete

To extract the DEM:

1. Click Extract DEM.

A Progress Monitor opens which shows the status of the extraction process. After the extraction is complete, the Progress Monitor closes. A message box opens with the message DEM Extracted successfully.

2. Click Close.

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To view a report of the resulting DEM file:

1. From the AIRPHOTO folder, open S129_S130_dem.rpt and S188_S189_dem.rpt in a Text Editor.

2. View the report to see how the extracted DEM elevations compare with the elevations that were entered with your GCPs.

Note

Even though you chose to interpolate failed areas during DEM extraction by selecting the Fill Holes & Filter field in the Extraction Options section, you may still need to manually edit the DEM.

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Editing the DEM

Digital elevation models (DEMs) may contain pixels with failed or incorrect values.

You edit the DEM to smooth out the irregularities and create a more pleasing DEM.

For example, areas such as lakes often contain misleading elevation values so

setting those areas to a constant value improves your model.

To open the 2D DEM Editing window:

1. From the DEM From Stereo toolbar, click Manually edit generated DEM.

A Focus window and the DEM Editing window open.

Figure 3-8: DEM Editing window

2. Click Browse.

3. From the AIRPHOTO folder, select GeocodedDEM.pix and click Open.

4. From the Layer list, select channel 2.

The DEM is loaded in the Focus view area.

5. For the current project, click Close at the bottom of the 2D DEM Editing window.

Note

DEM editing is described in more detail in the ASTER exercise of the OrthoEngine Workbook.

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Figure 3-9: Focus window displaying the geocoded DEM

In this lesson you:

• Set up to extract and geocode a DEM

• Extracted and geocoded the DEM

• Edited the DEM

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• Build a raster DEM from contour data

• Define the georeferencing of the output DEM

This lesson describes how to build a raster DEM from contour data. To complete

this lesson you require:


• The file ap_contours.pix that contains contour information in a vector layer

OrthoEngine can calculate the elevations from vector layers to generate a raster

digital elevation model (DEM), which is saved as a PCIDSK file (.pix). OrthoEngine

uses raster DEMs to orthorectify images. If your elevation data is stored as vectors

such as contours, points, TIN, or even a text file containing coordinates, you can

convert them into a raster DEM as long as the vectors are in any of the supported

formats.

Note

You can combine vectors from different layers and files to generate a DEM.

Vector layers can contain:

Points: A point is a single coordinate (x, y, and z).

Lines: A line is a start and end coordinate with points in between to define the

shape.

Polygons: A polygon is a line with the same start and end coordinate forming an

area with numerous points along the line to define its size and shape.

Contours: A contour is a line formed by a set of points representing the same

value of a selected attribute. Contours are usually used to represent connecting

points on the ground with the same elevation.

TIN: A Triangulated Irregular Network (TIN) is a digital model of adjoining triangles

formed from points selected on the terrain to represent an accurate model of the

surface. The TIN model can contain coordinates and other geographical data.

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Setting up to build a DEM

To open the Import & Build DEM window:

1. On the OrthoEngine window in the Processing step list, select Import & Build DEM.

A new toolbar with six icons appears. These are tools for importing a building a DEM from a raster file, DEM from GCPs/Tie points/Elevation Match points, DEM from vectors/points, DEM from contours, DEM from a TIN, and manually edit the generated DEM.

Figure 3-10: Import & Build DEM toolbar

2. On the Import & Build DEM toolbar, click DEM from contours.

The Input Vector Layer Selection window opens.

Figure 3-11: Input Vector Layer Selection window

To import the vector file to generate the DEM:

1. In the Input Vector Layer Selection window, click Select.

2. From the AIRPHOTO folder select ap_contours.pix and click Open.

The available vector layers are listed.

3. Under Vector layer available, select 2 [VEC]: Contour and click the arrow.

The selected layer appears under Vector layers to interpolate.

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Note

You can have vector layers containing vectors, points, contours, or TIN from a variety of different files in the list of vectors to be interpolated.

4. In the list of Vector layers to interpolate, select the 2 [VEC]: Contour layer.

5. In the Data type list, select Contours.

This is the type of vector contained in this layer.

6. For the Elevation source, select ELEVATION.

This is the attribute field that stores the elevation values.

Figure 3-12: Input Vector Layer Selection Window

7. Click OK.

The Input Vector Layer Selection window closes and the Define Output DEM File window opens.

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Figure 3-13: Define Output DEM File window

Defining the output DEM file

After importing the source for generating the digital elevation model (DEM), you

determine the parameters of the DEM output.

To define output DEM file:

1. In the Output DEM box, enter BuiltDEM.pix.

2. To generate a DEM that covers the area where elevation data exists, click Elevation Source Area.

Mosaic Area will generate a DEM that covers the area defined by the Mosaic Area.

Photo Extents will generate a DEM that covers the extents of all the images in the project. This is useful when you want the DEM to cover the images being orthorectified, but extrapolating beyond the elevation source area can cause significant errors in your project.

3. Click Generate DEM.

The Iteration Values window opens.

4. Use the default values of 10 Iterations and a Tolerance of 1.00.

The No. of Iteration is the maximum number of times that the DEM is smoothed. The Tolerance is the minimum difference in value required during smoothing to warrant another application.

The output DEM opens in a viewer.

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Figure 3-14: Built DEM

In this lesson you:

• Built a raster DEM from contour data

• Defined the georeferencing of the output DEM

Checkpoint

Module 4 You can now proceed to Module 3: DEM operations. In this module, you learn how

to set up and correct your images or photographs for distortions due to camera tilt

and terrain relief.

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Orthorectification

Module 4 has one lesson:

Lesson 4.1 Generating the orthorectified images

Lesson 4.2 Adjusting orthorectified images

The data correction stage

The Data Correction stage consists of the following modules:



The objective of this module is to correct a set of airphotos so the resultant

orthophotos can be used in the mosaicking process in Module 5: Mosaicking.

Module

4

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Geomatica OrthoEngine - Module4: Orthorectification


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• Set up for orthorectification

• Generate the four ortho photos

This lesson describes how to set up and perform orthorectification using aerial

photographs.

For this lesson you require:

• The airphoto.prj file that you used in Module 2 to compute the math model.

Alternatively, you may open the airphoto_model.prj file from Module 3, which contains the four airphotos from the Richmond Hill dataset. The project contains all the required GCPs and tie points and an up-to-date model

• The ap_dem.pix DEM file.

Orthorectification is the process of using a rigorous math model and a digital

elevation model (DEM) to correct distortions in raw images as shown in the figure

below. The rigorous math models, such as the Aerial Photography or Satellite

Orbital math models, provide a method to calculate the position and orientation of

the sensor at the time when the image was taken. The DEM is a raster of terrain

elevations.

The quality of the orthorectified image is directly related to the quality of the

rigorous math model and the DEM. A poorly computed math model, an inaccurate

DEM, or a DEM incorrectly georeferenced to the math model will cause errors in

the orthorectified images.

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Figure 4-1: Using sensor geometry and a DEM to orthorectify imagery

Setting up for orthorectification

The Ortho Image Production window lets you set up and schedule the ortho production. Several images can be selected and processed in one step.

Images to process

To set up the photos:

1. On the OrthoEngine window in the Processing step list, select Ortho Generation.

A new toolbar appears containing one icon to schedule the generation of the ortho images.

Figure 4-2: Ortho Generation toolbar

2. Click Schedule ortho generation.

The Ortho Image Production window opens.

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Figure 4-3: Ortho Image Production window

3. Under Available images, use the SHIFT key to select all four photos and click the arrow button to move the images under Images to process.

The images are processed in the order that they are listed.

4. Under Images to process, select S129.

By default, the ortho image will be named oS129.pix. You could also enter a different filename in the Ortho Image section.

Selecting the DEM

To select and load the DEM:

1. Under DEM, click Browse.


The Database Channels window opens.

3. For the Background elevation, enter -150 and click OK.

This represents “No Data” pixels in the DEM. For DEMs generated by OrthoEngine, the background elevation defaults to -150. Other DEMs have different background elevation values that you must know before they can be used. If you do not know the background value, click DEM Info in the DEM File window. The window displays the three lowest and three heights values in the DEM.

Note

It is critical that the background elevation be set to the correct value, if there are areas of no data in the DEM.

Elevation Scale: This is used to convert the pixel values in a digital elevation model (DEM) into their correct elevation value. For example: since an 8-bit channel can only contain integers between 0 and 255, you may have a DEM that was multiplied by 10 to maintain the decimal precision of its elevation values. A DEM

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pixel may have a value of 102, but the actual elevation that it represents is 10.2. To convert the DEM pixel value from 102 to 10.2 you must multiply it by 0.1. Therefore, you type 0.1 in the Elevation scale box to convert the DEM pixels back to their true values.

Elevation Offset: This is used to add a value to the pixel values in a DEM to obtain their actual elevation value. Perhaps the DEM pixel with a value of 102 actually represents an elevation value of 1,102. To store the elevation values in an 8-bit channel, 1,000 was subtracted from all the pixel values when the DEM was created. Therefore, you must type 1,000 in the Elevation offset box to restore the true values.

You can also use Elevation offset to adjust the elevation reference of a DEM. The elevations in a DEM can be calculated above Mean Sea Level or an ellipsoid. The elevation reference in the DEM must match the elevation reference of the imagery that you want to orthorectify. To compensate for a discrepancy, you can type the difference between the two elevation references in the Elevation offset box.

Tip

If you do not have a DEM, you can use the average elevation of an area to orthorectify the image. However, this will not produce results as accurate as using a DEM. Type the average elevation in the Elevation offset box.

Working cache This is the maximum amount of RAM that you allocate for the ortho generation process. The limit should not be more than half the available RAM. Specifying more than half may significantly reduce performance.

To set up the working cache:

• Enter an amount of RAM appropriate for your computer.

Sampling interval

The Sampling Interval controls how the computations are performed when an image is orthorectified or geometrically corrected. When an image is corrected, OrthoEngine selects a pixel from the output file, computes the elevation from the DEM (if available), applies the math model to determine which pixel location it corresponds to in the raw image, and then transfers the data to the pixel in the output file. The Sampling Interval determines how many output pixels are computed following this method. A Sampling Interval of 1 means that the position of every output pixel is processed. To speed up the process, you can increase the Sampling Interval. Note that this sampling interval interpolates the position of the pixels, not the intensity of the pixels.

To set up the sampling interval:

• Enter a value of 4.

This means that the correction for every fourth pixel is calculated and the correction for the pixels in between are interpolated.

Selecting the resampling method

Resampling extracts and interpolates the gray levels from the original pixel locations to the corrected locations.

Nearest Neighbor: Identifies the gray level of the pixel closest to the specified input coordinates and assigns that value to the output coordinates. Although this

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method is considered the most efficient in terms of computation time, it introduces small errors in the output image. The output image may be offset spatially by up to half a pixel, which may cause the image to have a jagged appearance.

Bilinear Interpolation: Determines the gray level from the weighted average of the four closest pixels to the specified input coordinates and assigns that value to the output coordinates. This method generates an image with a smoother appearance than Nearest Neighbor Interpolation, but the gray level values are altered in the process, which results in blurring or loss of image resolution.

Cubic Convolution: Determines the gray level from the weighted average of the 16 closest pixels to the specified input coordinates and assigns that value to the output coordinates. The resulting image is slightly sharper than one produced by Bilinear Interpolation, and it does not have the disjointed appearance produced by Nearest Neighbor Interpolation.

To select the resampling method:

• In the Resampling list, select Cubic.

Auto clip edge This option removes a specified percentage of the image’s outside edge. You can use this option to remove unwanted areas such as the data strip and fiducial marks from aerial photographs or a dark perimeter or distortion along the edge of the image.

Generating the orthos

You have two choices for the ortho generation:

• You can generate your orthos now

• You can generate your orthos at a later time

If you intend to automatically mosaic the processed images, you can click Close instead of Generate Orthos. When you set up the Automatic Mosaicking window, select Regenerate offline orthos, and OrthoEngine will process the images and mosaic them in one step.

In this lesson, you will generate the orthos now.

To generate the ortho photos now:

1. Click Generate Orthos.

The Ortho Production Progress monitor opens and shows the status of the orthorectification process for each photo. After all the orthos are generated, the following message appears: All Processing Completed.

2. Click Close.

The message Ortho done appears beside each photo in the Available images section, indicating that the original photos are now orthorectified. The files containing the corrected photos are named oS129.pix, oS130.pix, oS188.pix, and oS189.pix.

Tip


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In this lesson you:

• Set up for orthorectification

• Generated the four ortho photos

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From time to time, you may have an ortho image that is of exceptional quality

(cloud-free) but for one reason or another, the accuracy of the ortho image is not

satisfactory. This may be due to a number of reasons, such as the ortho image was

provided by another facility or source. The ground control used to correct the

ortho image may have been inaccurate. There can be many different causes, and

the user has two options, attempt to find a replacement image, or adjust the

ortho image to meet the accuracy measurements.

In this lesson, you will be adjusting a Landsat 5 TM image that is inaccurate by

more than 1000m (approximately 35 km to be exact) with a 2nd Landsat 5 TM

ortho image that is reasonably accurate but has some clouds. The overlap

between the two ortho images is adequate to collect enough GCPs to properly

adjust the ortho image.


• Create a new project to adjust orthorectified images

• Add ortho images to the project

• Collect GCPs on the ortho image that needs correction

• Adjust the ortho image

Creating a new project to adjust orthorectified images

To create a new project in OrthoEngine:

1. From the main OrthoEngine panel, select File | Save to save your current open project.

2. From the main panel, select File | New to launch the Project Information panel.

3. Select the Adjust Ortho radio button.

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Figure 4-4: OrthoEngine Project Information window

4. Select the Browse button and enter the project name AdjustLandsatOrtho.prj.

5. In the Name field, enter Adjust ortho image.

6. In the Description field, enter Adjust Landsat 5 TM ortho image.

7. Click the OK button.

The Set Projection window opens.

8. Enter the projection values as shown in Figure 4-5:

Figure 4-5: OrthoEngine Set Projection window

9. Click the OK button.

Tip

Now is a good time to save your project file.

Adding a new ortho image to the Adjust ortho image project

To add a new ortho image to the Adjust ortho image project:

1. From the Processing step drop-down list, select GCP Collection.

2. Select the Open Image icon beside the GCP Collection drop-down list.

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3. In the Open Image panel, select the New Image button and navigate to the UpdateOrtho subdirectory.

4. Select the file named oL5_20070604_22078_BAD.pix.

5. Click Open.

Figure 4-6: OrthoEngine Open Image window

6. Select the file in the panel, and click the Open button.

7. Load the band combination of 5, 4, 3 as RGB.

8. Close the Open Image panel.

Collecting GCPs to adjust an ortho image

To collect GCPs on an ortho image:

1. From the GCP Collection processing step, click the Collect GCPs Manually icon.

2. On the GCP Collection panel, choose a Geocoded image as the Ground control source.

3. Select the file oL5_20100204_220078.pix as the geocoded image.

4. Load the RGB as bands 4, 3, 2.

Note

It is easier to keep track of the ortho-to-correct and the accurate ortho image if the RGB band combination is different.

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Figure 4-7: OrthoEngine GCP Collection window and image viewers

In this lesson, we will manually collect enough GCPs to correc the ortho image. We will start by collecting GCPs as close to the four corners as possible.

5. In the Geocoded Image panel, place your cursor as close as possible to the feature displayed in Figure 4-8:.

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Figure 4-8: Geocoded image displayed in OrthoEngine

6. Zoom to 1:1 resolution or more to fine-tune the position of the crosshair.

Figure 4-9: Geocoded image, zoomed to 1:1 resolution

Figure 4-10: Geocoded image, zoomed in

7. Click on the Use Point button.

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The Longitude and Latitude values are transferred to the GCP Collection panel.

8. Locate the same position in the inaccurate ortho image.

9. When you have found the same position, click the Use Point button.

The image pixel/line coordinates are transferred to the GCP Collection panel.

Now is a good time to save your project.

10. Using the same steps (Step 5 through Step 9), locate a point common to both images in the lower-right corner. Refer to the following images for guidance.

Figure 4-11: Geocoded image, zoomed to 1:1 resolution

Figure 4-12: Geocoded image, zoomed in

11. Repeat the procedure to collect another two ground control points in the upper-left corner of the image, followed by the lower-left corner of the image.

To assist you in locating the corner points, you can find a reference polygon vector within the reference image oL5_20100204_220078.pix. You can load the polygon as an approximate guide to the four corners of the oL5_20070604_22078_BAD.pix image with respect to the corresponding coordinates for the reference image.

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12. On the Geocoded Image Viewer panel, click the Load Vector button.

13. In the General Information panel, click the Load button.

14. Locate the file oL5_20100204_220078.pix.

15. Select vector segment 2, New Whole Polygon Layer.

Figure 4-13: General Vector Information window

16. Continue to collect GCPs, saving your project frequently, until you have collected a minimum of 20-30 well-distributed GCPs with an RMS error less than 2.0 pixels.

Note

Depending on the accuracy and resolution of the ortho image to adjust, including the accuracy of the DEM that was used to perform the original orthorectification, it is unlikely that you will get an RMS error less than 1 pixel.

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Figure 4-14: GCP Collection window

17. Once you have collected enough GCPs, go to the Geometric Correction processing step.

Tip

Now is a good time to save your project file.

Geometrically correcting an ortho image

To geometrically correct an ortho image:

1. In the Geometric Correction Processing Step, click the first icon, Open Image.

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Figure 4-15: Geometric Correction Open Image icon

2. Select the image and click Quick Open & Close.

The image is opened as RGB band 1, band 2, band 3.

3. If you wish, use the RGB Mapper tool to change the FCC to TM bands 5, 4, 3 (or any other preferred band combination). This step is optional.

4. In the Geometric Correction Processing step, click the second icon, Define bounding polygons.

We will set the clip area to the entire area and trim the edges in Focus after we have inspected the updated ortho image. The areas outside the GCP collection area can have significant distortion, depending on the accuracy of the initial file and the GCPs used to create the updated model. Because the initial ortho image was significantly inaccurate, you should expect large distortions in the updated ortho image along the edges of the image and outside the GCP collection area.

Figure 4-16: Define bounding polygons

5. Click the Reset button to define the entire area, then click the Close button.

Now is a good time to save your project.

6. In the Geometric Correction Processing Step, click the third icon, Schedule geometric correction.

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Figure 4-17: Geometric Corrected Image Production window

7. From the Available Images area, select the oL5_20070604_22078_BAD image and click the -> button to transfer the image to the Images to Process area.

8. In the Corrected Image area, change the name of the file by removing the word BAD and removing the initial letter o.

Figure 4-18: Changing the name of the corrected image file

9. In the Processing Options area, set the Working cache to a reasonable amount (approximately half to three quarters of the available RAM).

10. Choose a Sampling interval of 3 or 4.

Note

Although a sampling interval of 1 produces the most exacting results, it requires more processing time. A sampling interval of 2-4 will produce results that are virtually indistinguishable from those of a sampling interval of 1, but will take less processing time.

11. Set the Resampling method to Cubic Convolution.

Figure 4-19: Processing options

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12. Click the Correct Images button.

Depending on your computer’s resources, the process can take from 10-15 minutes or longer.

13. When the Geometric Correction process has finished, save the OrthoEngine project and examine the results in Geomatica Focus.

Viewing and performing quality control on an adjusted ortho image

To view and perform QC on an adjusted ortho image:

1. Launch Geomatica Focus and add the following two files:

• oL5_20070604_22078.pix

• oL5_20100204_220078.pix

You will notice that the edges of the image are badly distorted. We will use Focus to trim off the edges of the ortho image.

Figure 4-20: Viewing images in Geomatica Focus

2. Change the FCC to an RGB of 5, 4, 3 or your preferred FCC combination.

3. From the Files Tree, right-select the vector layer in oL5_20100204_220078.pix and click View.

4. Use the Pan and Zoom tools to perform a quality control check of the updated ortho image within the vector polygon.

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5. If an area has significant errors in accuracy, use the vector editing tools to edit the vector.

6. When you have completed your QC activities, it is time to trim the edges of the ortho image using the vector polygon.

7. Ensure that the vector polygon is selected.

Clipping/subsetting an adjusted ortho image

To clip an adjusted ortho image:

1. From the main Focus menu, select Tools | Clipping/Subsetting.

2. In the Clipping/Subsetting panel, place a checkmark in the checkbox beside Rasters.

3. Enter an Output file name: oL5_20070604_22078_trim.pix

4. In the Define clip region area, choose Select a Clip Layer as the Definition Model.

5. Select the file oL5_20100204_220078.pix as the File.

6. Select the Vector layer as the Clip layer.

The extents of the vector layer will appear as the area to clip. We must, however, clip the file using the boundary of the shape.

Figure 4-21: Focus Clipping/Subsetting window

7. Click on the Shape(s) Boundary radio button and place a checkmark in the checkbox beside Clip using selected shapes only.

If the vector is not selected, the checkbox will appear grayed-out; ensure that the vector is selected.

The vector’s shape boundary will now appear in the overview window for the output raster.

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Figure 4-22: Clipping using selected shape

8. Click the Clip button.

A progress monitor appears; when the process is complete, the file will be clipped.

9. View the clipped file in Geomatica Focus.

Figure 4-23: Viewing clipped file in Geomatica Focus

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In this lesson you:

• Created a new adjust ortho project

• Set up project parameters (projection, etc.)

• Added an image to the project

• Manually collected GCPs using a reference image

• Geometrically corrected the ortho image using the manually collected GCPs

• Examined the adjusted ortho image using Focus

• Trimmed the edges of the adjusted ortho image to remove edge distortions in the data

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Checkpoint

Module 5 Automatic Mosaicking

In Module 5 you will define an area for mosaicking, and then automatically mosaic

the orthorectified photos contained in your project file.

Module 5 Manual Mosaicking

If you would like more control over the mosaicking of your corrected photos, or if

you do not have the OrthoEngine Productivity Tools package, proceed to the

manual mosaicking lesson in Module 5.

In Module 5 you define an area for mosaicking, and then interactively mosaic the

orthorectified photos contained in your project file.

Additional Operations

If you need additional operations to be performed on your photographs, proceed to

Geomatica for cartographic production and quality assurance applications.

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Mosaicking


Lesson 5.1 Defining a mosaic Area

Lesson 5.2 Manual mosaicking

Lesson 5.3 Automatic mosaicking

Data correction stage

The Data Correction stage consists of the following modules:



Mosaicking is joining together several overlapping images to form a uniform

image as shown in the figure below. It is similar to creating a jigsaw puzzle with

your images, and then making the seams disappear.

For the mosaic to look like one image instead of a collage of images, it is

important that the images fit well together. You will achieve better results if you

orthorectify your images. Using a rigorous math model ensures the best fit not

only for the individual images, but for all the images united as a whole.

To achieve that seamless look in the mosaic, place the seams, called cutlines,

where they will be the least noticeable and select images or portions of images

that are not radically different in color.

Module

5

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Geomatica OrthoEngine - Module5: Mosaicking

Figure 5-1: Mosaicking

Manual mosaicking

You can use Manual Mosaicking to create your mosaic one image at a time, to edit

the cutlines in an automatically mosaicked project or to replace unsatisfactory

areas in the mosaic. For each image that you want to include in the mosaic file, you

must complete four steps in sequence; select an image to add, collect the cutline,

adjust the color balance and add the image to the mosaic area.

Automatic mosaicking

Although you can create your mosaic one image at a time by using Manual

Mosaicking, most of the time you will use Automatic Mosaicking to do the bulk of

the work, and you will use Manual Mosaicking to edit portions of the mosaic file.

Some projects may require more editing than others such as those containing large

bodies of water or urban areas with buildings leaning in different directions. In

addition to reducing your work load, Automatic Mosaicking will often produce a

more seamless look than if you had attempted to create the mosaic by hand.

Both manual and automatic mosaicking will be described in detail in this module although as stated above, automatic mosaicking would generally be used to do the bulk of the work. Manual mosaicking could then be used to edit the cutlines.

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PCI Geomatics 5-3




• Define an area for mosaicking

To define an area for mosaicking, you require:

• The file airphoto.prj that you used in Module 4 to generate the ortho images.

Alternatively, you can open airphoto_ortho.prj from the AIRPHOTO folder. This project contains all the required ground control points and tie points, and the photos are already orthorectified.

The objective of this lesson is to set up an area that includes a part of each of the

four orthophotos. The defined area is used in the mosaicking process.

Defining a mosaic area

The Mosaic Area determines the extents of the mosaic file. The images are added

to the Mosaic Area like pieces of a puzzle. On the Define Mosaic Area window, the

footprints of the images in your project are displayed as they overlap. The

crosshairs represent the principal point of each image. Click one of the crosshairs

to reveal the footprint of an individual image. The background value of the Mosaic

Area is zero by default.

Before you create a mosaic, you need to define an empty mosaic file.

To define the mosaic area:

1. On the OrthoEngine window in the Processing step list, select Mosaic.

A new toolbar with four icons appears. The toolbar contains functions for defining a mosaic area, manual mosaicking, reapply mosaicking, and automatic mosaicking.

Figure 5-2: Mosaic toolbar

2. On the Mosaic toolbar, click Define mosaic area.

The Define Mosaic Area window opens.

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Figure 5-3: Define Mosaic Area window

By default the bounds of the Mosaic Area are the maximum extents of the images in the project. The size of the mosaic area can be changed manually using your mouse.

3. Enter the Define Mosaic Area mode by clicking the Define Mosaic Area icon in the upper left corner of the Manual Mosaic tool.

4. Place the cursor over the side or corner of the frame inside the Mosaic Area and move it to change its size and shape.

Figure 5-4: Define Mosaic Area, resizing the mosaic extents

5. Use the mouse cursor to change the size, shape, and position of the mosaic area. Define a broad area that includes part of each photo in the project.

Alternatively, use the Mosaic Extents section of the window to enter specific corner coordinates and define the size of the mosaic area in pixels and lines.

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Figure 5-5: Define Mosaic Area window after the area has been adjusted

6. Move the cursor inside the Mosaic Area to move and resize the frame. Define a broad area that includes part of each photo in the project.

7. In the Mosaic File section of the Define Mosaic dialog, click Browse.

The file selector window opens.

8. For the name of the file, enter manual_mosaic.pix and click Save.

If the Create Later option is unchecked, OrthoEngine creates the new mosaic file and stores it in the folder where your project is saved. If this option is checked, the mosaic file will be created later.

Alternatively, you may select an existing mosaic file.

To select an existing mosaic file:

1. In the Define Mosaic window, click the Define Mosaic Area From File icon in the toolbar.

The File Selector panel opens.

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Figure 5-6: File Selector window

2. Select the Mosaic File air_mos.pix and click Open.

Note

When you select an existing mosaic file, the Create Later option is disabled.

Selecting images for mosaicking

By default, all images in the project are selected; these appear as green frames in

the Mosaic Area window. You may choose to select fewer images for the mosaic.

To select the images to be mosaicked:

1. From the Define Mosaic window’s toolbar, click the Select Images To Be Mosaicked icon.

2. Using the mouse cursor, select the first image to include in the mosaic (oS129).

3. Press the Ctrl key as you select images oS130 and 0S189. Images included in the mosaic appear as green frames; excluded images appear as blue frames.

Ensure that the mosaic area overlaps at least part of the selected images, as shown below.

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Figure 5-7: Selecting images to include in mosaic

The following image shows an incorrect mosaic definition because the mosaic area overlaps only sections of unselected images; this mosaic area will display an error message.

Figure 5-8: Incorrect mosaic area definition — no overlap with selected images

4. Click OK.

OrthoEngine creates or saves your mosaic file and stores it in the folder where your project is saved.

You are now ready to mosaic your photos.

With OrthoEngine there are two methods for generating a mosaic:

• Manual mosaicking

• Automatic mosaicking

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Tip


Manual mosaicking

First, use the manual process in Lesson 5.2: Manual mosaicking to create a seamless final product from the four orthophotos. Here, you use a set of manual tools, which affords more precise control.

Automatic mosaicking

After this process is complete, proceed to Lesson 5.3: Automatic mosaicking and mosaic the same set of orthophotos using the automatic tools.

In this lesson you:

• Defined an area for mosaicking

• Selected images for mosaicking

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• Select images to add to the mosaic

• Collect and edit cutlines

• Perform color balancing

• Generate the mosaic

To create the mosaic from the orthophotos, you require:

• The project file from Lesson 5.1

Mosaicking the first image

After defining an area, begin manual mosaicking by selecting the first photo to add to the mosaic file.

To open the Manual Mosaicking window:

• On the Mosaic toolbar, click Manual mosaicking.

The Manual Mosaicking window opens.

Figure 5-9: Manual Mosaicking window

Project image files

Images within the current project are listed in the Tree List. Images are identified by a crosshair within the viewer. Your mosaic file is empty; therefore, you do not need to collect cut line for the first image. The first image, oS129.pix, will be added to the mosaic file in its entirety.

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Collecting and editing cutlines

When you create a mosaic, you want to crop the images so the best portions of the images are seamlessly joined together. A cutline is a polygon that outlines the portion of an image that will be used in the mosaic.

As the cropped images are added to the Mosaic Area, the data in overlapping areas is covered by the most recent addition. Areas where several images overlap provide you with more opportunities to find the best location for the cutlines. When you save the project, the cutlines are saved with their corresponding images.

To make the seams between images less visible, select features that are consistent in tone and texture, low to the ground, uniform in appearance, and conspicuous, such as roadways and river edges. Features that display clear boundaries provide a natural camouflage for the seam.

Avoid:

• buildings or man-made features, since they may lean in different directions in the imagery

• large bodies of water, because waves may look different in different images, and water tends to have different color in different images

• areas that are significantly different in color and texture, such as forests and cultivated land, since they may look different from image to image

Collect cutlines

To collect cutlines:

1. From the tree list, select the oS129.pix image, and turn off the images oS188.pix and oS189.pix.

2. Right-click oS130.pix to display the context menu, and select New Cutline.

Alternatively, click the New Cutline icon on the toolbar.

Figure 5-10: New Cutline context menu item

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A crosshair with polygon appears when the mouse is placed over the corresponding image.

3. Using the zoom tools, zoom to a road that runs through the area of overlap.

4. Using your mouse, create a cutline for the area of the oS130.pix image that you wish to include in the mosaic.

Clicking with the mouse will add a vertex to the polygon. Double-click to complete the cutline.

Figure 5-11: New cutline created

The collected cutlines appear as vector layers under the corresponding image label in the tree list.

The changes resulting from cutline collection are shown in the Mosaic Tool view area as the changes take place.

Edit cutlines

To edit a cutline:

1. From the toolbar, click the Vector Editing icon.

The Vector Editing Tools panel opens.

Figure 5-12: Vector Editing Tools panel

2. Use the Vector Editing tools to move, delete, add new vertices, and reshape a cutline. Click Show Vertices to make all vertices on the selected cutline visible.

3. Click on the vertex that you wish to edit and move it using the mouse.

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Figure 5-13: Editing a cutline

4. Move your mouse cursor to the road with the cutline, then click Zoom to 1:1 Image Resolution. As you can see, that part of the cutline is off the road, and the line does not have enough vertices to correct it.

Figure 5-14: Correcting a cutline

5. Click the Add Vertices icon on the Vector Editing Tools panel and move the cursor to the cutline. Each mouse click adds a new vertex to the cutline.

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Figure 5-15: Adding new vertices to the cutline

6. Add 5 or 6 vertices to your cutline and move the new vertices to position the cutline on the road. The cutline is now correct, but it also contains a few unnecessary vertices.

7. Click on the vertices that you don’t need, and press the Delete key on your keyboard to remove them.

An alternate method for fixing this cutline would be to use the Reshape tool. This tool allows you to conveniently insert and move vertices along a cutline.

8. Click the Reshape tool on the Vector Editing Tools panel.

9. Using your mouse, click on the image near a cutline where you want the new reshaped cutline to be placed.

10. Continue to click along the feature in the image where the cutline should be located.

A thin black line will appear; this represents the reshaped cutline. A circle will also be located along the existing cutline, to indicate which cutline vector/vertices will be reshaped.

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Figure 5-16: Reshaping a cutline

Remember that it does not matter on which side of the existing (incorrect) cutline you click. Because the goal in this case is to correct an existing cutline, click on the feature where the cutline should go.

Figure 5-17: Reshaping a cutline

In Figure 5-17:, notice that the user digitized in the center of the road and crossed the cutline numerous times.

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Figure 5-18: Reshaped cutline

The end result is a reshaped cutline that correctly follows the feature, as modified by the user.

11. To add, move, or select vertices, you may also use the Vertices window. Select vertices on your cutline, click Show Vertices on the toolbar, then click Vertices (X,Y).

The Vertices window opens.

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Figure 5-19: Vertices window

12. Select a vertex from the table; this vertex will also appear selected in the Mosaic Area display. Selecting a vertex on the cutline also selects it in the Vertices table.

13. From the Vertices window, click the + button. A new vertex is added to the cutline. Add a few more vertices to correct your cutline.

14. Select a vertex and click the - button to delete it.

15. Select and expand a layer in the Layer List of the Mosaic Tool window by clicking the expand button to the left of the layer.

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Figure 5-20: Cutline context menu

16. Right-click the cutline layer. The following editing tool are available:

• Load Cutline to load an existing vector cutline layer

• Delete Cutline to remove the cutlines created for that particular layer

• Set Blend Width to specify the blend width for the image and the neighboring images

Load cutline You can load a previously exported cutline, or any polygon vector.

To load a cutline:

1. From the context menu described above, click Load Cutline.

The Import Cutline window opens.

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Figure 5-21: Import Cutline window

2. Click Browse to select the file that contains the cutline.

3. In the Shapes section, select the desired layer and the polygon shape within that layer, then click OK.

The cutline appears in the Mosaic Area display.

Blend seams Blending reduces the appearance of seams by mixing the pixels values on either side of the cutline to achieve a gradual transition between the images.

In Automatic Mosaicking, OrthoEngine blends the seams automatically. In Manual Mosaicking, the Blend Width option determines the number of pixels on either side of the cutline that are used to blend the seam. In areas containing bright or significantly different features, however, setting the Blend Width too high may cause "ghosting" or doubling of the features.

To set the Blend Width:

1. Expand the oS129.pix image in the Tree Layer.

2. Right-click the Cutline vector layer to display the context menu, and select Set Blend Width. Set the Blend Width to 3 pixels.

3. Click OK.

A blend width of three to five pixels is recommended for most mosaicking projects.

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Adjusting the color balance

Radiometric differences between images can cause a patchwork effect in a

mosaic. Color balancing evens out the color contrasts from one image to another

to reduce the visibility of the seams and produce a visually appealing mosaic.

You can adjust the color balance in Manual Mosaicking by collecting samples in

the overlap between the images already mosaicked and the image that you are

adding to the mosaic. OrthoEngine uses these samples, which are referred to as

Match Areas, to compute a look-up table (LUT) that will adjust the color in the

image that you are adding to match the images already mosaicked.

Collect small match areas representing the different areas so the look-up table can

be used to accurately correct radiometric mismatches. For example, collect a

match area in green areas to balance greens, a match area in dark areas to match

dark values, a match area in urban areas to match urban areas, and so on. Using

a single large match area covering a large part of the image is effective only if you

have an overall bright or dark difference between the images.

Create match areas

To create match areas:

1. Select the oS130.pix image in the Layer List and click the New Match Area button to begin the color balancing process.

Alternatively, right-click the oS130.pix image to display the context menu, and select New Match Area.

The crosshair and polygon icon appears.

2. Draw a polygon to define the match area on the selected image in the Mosaic Tool window.

The polygons over the match areas will be filled with the color corresponding to the cutline for the image chosen.

Note

You can edit match areas using the same procedure as described for editing cutlines. See “Edit cutlines” on page 12.

3. To apply the color balancing to the image, click the dropdown on the color balancing button and select Manual Area.

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Figure 5-22: Color Balancing, Manual Area

4. Define several match areas using the manner described above.

Any number of match areas can be defined, and are displayed in the viewer. As you collect Match Areas, the mosaic window displays the changes in the color balancing.

After the color balancing step is completed and you are satisfied with the results displayed in the mosaic window, proceed to generating the mosaic.

Generating the mosaic

When you have collected cutlines and performed color balancing for the images to be mosaicked, you can add the image(s) to the output mosaic file.

To generate the mosaic:

1. From the Layers List, select all of the images by holding down the Crtl key and selecting each file. Select oS129.pix and oS130.pix.

2. On the toolbar, click Add Image(s) to Mosaic.

A progress monitor opens, indicating the image is being mosaicked into the mosaic area file.

When the process is complete, the mosaicked images are removed from the Tree List; the image layers are listed under the mosaic file. These images can be reprocessed.

You have now created a mosaic of two photos using manually collected cutlines. Continue to add the remaining photos.

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Adding and removing images

Add an image You can add images that you did not select when you defined the Mosaic Area.

To add an image to the mosaic:

1. From the toolbar, click the Add Image(s) to Session button.

The Add Image window opens, listing the images that are not currently in the mosaic Tree List.

2. Select oS129.pix and oS189.pix.

3. Click Add.

Both images, along with their cutlines, open. You can process these images as previously described, or remove them from the Tree List.

Remove an image

To remove an image from the mosaic:

1. From the Tree List, select the image that you want to remove.

2. Right-click to display the context menu, and select Remove Image.

The image is removed from the current session.

Note

Although you can select multiple images to add to the session, you may only remove a single image at a time.

Reprocessing imagesAfter the mosaic is complete, you may discover some areas that you want to change. You can use the Reprocess option within the Manual Mosaicking tool to edit the cutlines or adjust the color balancing for the images as required.

You can use the Reprocess option to:

• Reproduce missing mosaic files.

• Regenerate the mosaic at different resolutions.

• Create a subset of the mosaic by changing the size of the Mosaic Area and then regenerating the mosaic.

To regenerate the mosaic:

1. In order to reapply mosaicking, OrthoEngine must have access to a blank mosaic file to which the results will be written. To create this blank mosaic file, click on the Define Mosaic Area on the OrthoEngine toolbar.

2. Click the Define Mosaic Area button, and in the file field, enter a new file name for the blank mosaic file.

3. Click the Select Images to be Mosaicked button, and select all four of the ortho images. Once an image is selected, it should have a green footprint vector.

4. Uncheck the Create Later check box.

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5. Click OK.

A message box appears, stating that OrthoEngine is creating the output mosaic file.

6. On the OrthoEngine toolbar, click the Reapply Mosaic button under the Mosaic processing step.

In the Reapply Mosaicking dialog, you will see all of the files that comprise the final mosaic.

7. Place a check mark in the Use field next to each of the images.

8. Click Generate Mosaic to regenerate the mosaic.

A progress bar will appear stating that the mosaic is being regenerated.

Figure 5-23: Reapply Mosaicking window

In this lesson you:

• Selected images to add to the mosaic

• Collected cutlines

• Performed color balancing

• Added images to the mosaic

• Generated the mosaic

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• Define a new mosaic area

• Set up for automatic mosaicking

• Generate the mosaic

• View the mosaic

Mosaicking is the second step in the Data Correction stage. This lesson describes

how to set up the orthophotos, generate the mosaic automatically, and view the

result.

To create the mosaic from the orthophotos, you require:

• The project file from Lesson 5.1

• An new area defined for mosaicking

Defining a new mosaic area

Before creating your automatic mosaic, you must define a new file for the mosaic. Otherwise, the manual mosaic will be overwritten.

To define a new mosaic area:

1. On the Mosaic toolbar, click Define mosaic area.

The Define Mosaic Area window opens.

2. Click Define New Mosaic Area.

3. Place the cursor over the side or corner of the frame and move it to change its size and shape.

Alternatively, you can enter corner coordinates for the mosaic area.

Define a broad area that includes part of each photo in the project.

4. Click Define Mosaic Area.

The Mosaicking Tool opens.

5. For the name of the file, enter auto_mosaic.pix and click OK.

6. Click Close.

Mosaicking images automatically

Although you can create your mosaic one image at a time by using Manual

Mosaicking, most of the time you will use Automatic Mosaicking to do the bulk of

the work, and you will use Manual Mosaicking to edit portions of the mosaic file.

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Some projects may require more editing than others such as those containing large

bodies of water or urban areas with buildings leaning in different directions. In

addition to reducing your work load, Automatic Mosaicking will often produce a

more seamless look than if you had attempted to create the mosaic by hand.

The mosaicking process starts with the image selected in the Starting image list

and then adds contiguous images moving outward from Starting image. Once the

Starting image is selected, the list is sorted according to the order that they will be

added to the mosaic file. The Starting image also determines the order for color

balancing and cutline generation.

Now that you have defined a new mosaic area, you will create the mosaic using the automatic mosaicking tools. Afterwards, you will compare the manually generated mosaic with the automatically generated mosaic.

To open the Automatic Mosaicking window:

• On the Mosaic toolbar, click Automatic mosaicking.

The Automatic Mosaicking window opens.

Figure 5-24: Automatic Mosaicking window

Each row in the table represents an image in your project and lists the Image ID, Ortho ID, Status, whether the image is to be used what type of normalization is to be applied. By default, all images will be used in the mosaic.

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To set up the orthos for mosaicking:

1. Click in the Use column to ensure all images will be used.

Only the images with check marks in the Use column will be mosaicked.

2. Under the Normalization column, select None.

Normalization Normalization is used to even out the brightness in the images to achieve a more pleasing mosaic. You can set this feature differently for each image by clicking the corresponding arrow beside the Normalization column or you can set it for all the images by selecting the feature in the Normalization list and clicking Apply to All.

None: To leave the images as is.

Hot Spot: To remove hot spots from the image. A hot spot is a common distortion that results from solar reflections. Hot Spot normalizes the brightness over the image, but it does not remove spot reflections from lakes, cars, and buildings.

Across Image 1st Order: To correct the gradual change in brightness from one side of the image to the other. Recommended for ScanSAR and other imagery.

Across Image 2nd Order: To correct the gradual change from dark to bright to dark or vice versa across the image, also known as an “antenna pattern”. Recommended for ScanSAR and other imagery.

Across Image 3rd Order: To correct the gradual bright and dark patterns from one side of the image to the other. Recommended for ScanSAR and other imagery.

Mosaicking options

Regenerate offline orthos

You can select this option to regenerate orthorectified images with a Stale or Offline status. OrthoEngine can orthorectify the images and mosaic them in one step. If you have already generated your orthos, as outlined in the previous module, you do not need to select this option.

To regenerate offline orthos:

• Click Regenerate offline orthos.

Clear mosaic file before mosaicking

This options deletes existing data from the mosaic file. If you are already starting with a blank mosaic file or are adding images to a large existing mosaic, you can disable this option to leave the mosaic file as is, which can save time. Although regenerating a large mosaic just to add a few images may be time consuming, it may produce better color balancing results.

To clear this option:

• Click to clear the Clear mosaic file before mosaicking.

Since your mosaic file has not yet been created, there is no need to delete any existing data from the mosaic file.

Starting image The Starting image list lets you select the corrected image to be the basis for the mosaic, the color balancing, and the cutline selection.

To select the Starting image:

• In the Starting image list, select an image.

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Color balance Automatic color balancing applies tonal and contrast adjustments over the mosaic. There are three methods:

None: No color balancing is applied.

Entire image: The histogram of each entire image is used to compute the color balancing histogram. This method is recommended for images with low overlap or for images with systematic effects such as when images are bright at the top and dark at the bottom.

Overlap area: This method computes the color balancing histogram using only the pixels in the overlapping area of the images being added to the mosaic file. This method is recommended for most images.

Match to ref image: This method matches the color balancing for the mosaic to the image identified in the Mosaic reference image box.

To apply color balancing during mosaicking:

• In the Color balance list, select a method.

Ignore pixels under bitmap mask

Select Ignore pixels under bitmap mask to disregard the pixel values under the mask when calculating the color balancing histogram. OrthoEngine uses the last bitmap segment in each image file as the mask.

Cutlines Cutlines are drawn in areas where the seams are the least visible based on the radiometric values of the overlapping images. There are four methods:

Min difference: To place the cutline in areas where there is the least amount of difference in gray values between the images.

Min relative difference: To place the cutline in areas where there is the least amount of difference in gradient values between the images.

Edge features: To use a combination of Min difference and Min relative difference to determine the optimum location for the cutline.

Use Entire image: To mosaic images that do not overlap. OrthoEngine uses the four corner coordinates of the images as the cutlines to avoid gaps between the images.

If you want to use existing or imported cutlines, select Use existing cutlines.

To select a cutline method:

• From the Cutline Selection Method list, select Min Difference.

Blend width This is the number of pixels on each side of the cutline to delimit the area for smoothing the radiometric differences between the images. A Blend width of 5 will create a total blending width of 10 pixels - 5 pixels on either side of the cutline.

File options

Preview file The Preview file box specifies the path and file name for the file that will contain a low resolution version of the full mosaic. You can click Browse to change the default file name and select a location.

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Directory for temporary files

This box specifies the path for the temporary working files. You can click Browse to change the default location. The temporary files are deleted when the mosaic is complete.

External bitmap file

This option lets you select a mask area for your entire mosaic to exclude certain pixel values when calculating the color balancing histograms.

Mosaic reference image

This is the image you want to use as a reference for color balancing.

Generation Start Time

To set the Generation Start Time:

• Under Generation Start Time, click Start now or Start at (hh:mm) and set the time when you want the operation to begin (within the next 24 hours).

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Generating the mosaic

To generate a mosaic preview:

• Click Generate Preview.

This creates a low resolution version of the mosaic. It is saved in the file specified in the Preview file box. You can use the preview of the mosaic to verify the color balance and cutline generation before continuing with the full resolution version.

Figure 5-25: Automatic mosaic preview

To generate the mosaic:

• Click Generate Mosaic.

This creates the full resolution version of the mosaic. It is saved in the auto_mosaic.pix file you created.

Tip


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Viewing the mosaic

When you complete the automatic mosaicking, you can easily inspect the results in the image viewer or in Geomatica Focus.

To view the automatic mosaic:

1. From the File menu on the OrthoEngine window, select Image View.


2. From the AIRPHOTO folder, select auto_mosaic.pix and click Open.

A viewer opens and displays the mosaic file you created.

Alternatively, you can open the mosaic in a Focus window, or view the mosaic in the Mosaic Tool to make edits.

Figure 5-26: Results of automatic mosaicking

In this lesson you:

• Defined a new mosaic area

• Set up for automatic mosaicking

• Generates the mosaic

• Viewed the mosaic

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OrthoEngine componentization


Lesson 6.1 Data input and GCP collection

Lesson 6.2 Project creation and tie point collection

Lesson 6.3 Automatic GCP collection and mosaicking

OrthoEngine components

Many functions which were previously available only in the Focus and

OrthoEngine GUI environments have been componentized. Componentization

means that these functions can now be linked together into automated

workflows and run in batch processes. Automation and batch processing of

workflows is available through both visual modeling and command-line

scripting.

Component tasks include:

• Project creation

• Data import

• Ground control and tie point collection

• Radiometric adjustments

• Orthorectification

Modeler Geomatica Modeler provides an interactive methodology for the development

of both simple and complex data processing flows. Modeler provides access

to a number of standard operations such as data import and export, as well as

most EASI/PACE processing algorithms.

You build processing models by placing modules on the Modeler canvas and

then connecting the modules with pipes to create a process flow. You first

configure the modules and then execute the model in either single execution

mode or batch mode. During the execution of the model, graphical cues

indicate the data flow through the process. The Module Librarian enables quick

access to all modules.

Module

6

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Geomatica OrthoEngine - Module6: OrthoEngine componentization



• Launch Modeler and add modules to the canvas

• Connect the modules in the model

• Fill in the parameters for the Modeler modules

• Run the model in single execution mode

A common operation in desktop photogrammetry is the registration of new images

to existing geocoded images. Often this entails registration from a previous year’s

data, or the updating of a database with new overlapping imagery. Traditionally,

this registration was done through the manual or semi-automated collection of

ground control points. Geomatica 10 introduces automated ground control

collection through automatic image-to-image registration. Combined with

OrthoEngine’s accurate satellite and airphoto models this technology enables fast,

automated, rigorous orthorectification.

The model for this lesson uses a total of seven modules from the Module Librarian. First, you will add a CDLAND7 module to the canvas to import a Landsat-7 scene in HDF to pix format. The IMPORT modules will import a geocoded image and a digital elevation module.

AUTOGCP will be used to collect GCPs from the geocoded image. These GCPs will be refined with GCPREFN. SATMODEL applies the satellite model after which the image can be orthorectified and exported with ORTHO and EXPORT, respectively.

To start Modeler:

• On the Geomatica toolbar, click the Modeler icon.

Figure 6-1: Geomatica toolbar Modeler command

The Module Librarian and the Modeler workspace open on your desktop.

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Figure 6-2: Modeler workspace

Figure 6-3: Module Librarian

The Module Librarian provides access to the modules you can use to process your data. Modules are the basic building blocks for your model. You access modules from the Algorithm Library tree view in the Module Librarian. Modules are sorted into categories and subcategories according to their functionality and can also be listed alphabetically.

Note

Modules for which you are not licensed are identified with a lock icon.

Menu Bar

Toolbar

Canvas

Status Area

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To place a module on the canvas:

1. In the Module Librarian, expand the All Algorithms folder.

2. Select the CDLAND7 module.

The CDLAND7 graphical element displays in the Selected Algorithm area.

3. Click the CDLAND7 graphical element in the Selected Module window.

4. Click anywhere on the canvas.

The CDLAND7 graphical element displays on the canvas.

Tip

You can also click Add to Canvas to place the selected module in the canvas.

5. Add the following modules to the Modeler canvas:

• 2 IMPORT modules

• 1 SPLIT module

• 1 AUTOGCP module

• 1 GCPREFN module

• 1 SATMODEL module

• 1 ORTHO module

• 1 EXPORT module

Your canvas should appear similar to the one below.

Figure 6-4: Modules arranged on canvas

Now that all the modules have been added to the model, you will configure the IMPORT modules.

To configure the first IMPORT module:

1. Double-click the IMPORT module located below the CDLAND7 module.

The IMPORT Module Control Panel opens.

2. On the Input Params 1 tab, click Browse and locate the LANDSAT folder.

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3. Select geo_landsat.pix and click Open.

The Available Layers from this file are listed.

4. From the Available Layers list, select 3 [8U] red band.

Note

If using multispectral data for automatic image to image registration, you may choose to avoid using the blue band as it tends to be noisier than the other bands.

Figure 6-5: IMPORT Module Control Panel

5. Click Accept.

The output raster port glyph displays on the IMPORT module and the status indicator bar turns green.

To configure the second IMPORT module:

1. Double-click the second IMPORT module.



3. Select dem.pix and click Open.

The Available Layers from this file are listed.

4. From the Available Layers list, select the DEM from raster files layer.

5. Click Accept.

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To configure the CDLAND7 module:

1. Double-click the CDLAND7 module.

The CDLAND7 Module Control Panel opens.

2. On the Input Params 1 tab, click Browse.

The File Selector window open.

3. From the LANDSAT\raw folder, select L71048026_02620000923_HDF.L1G file and click Open.

4. In the CD Input Layer(s) List box, type 1, 2, 3, 4, 5.

5. In the File Description box, type Southern Vancouver Island.

Figure 6-6: CDLAND7 Module Control Panel

6. Click Accept.

The status indicator bar turns green.

You will now connect the modules with pipes.

Pipes Pipes are graphical elements that represent data transmission paths between modules. A pipe can be “thin” or “fat”. A thin pipe contains only one layer of information. A fat pipe, which is wider than a thin pipe, contains multiple layers of information.

Modules are connected by clicking the output port of the module to connect from and then clicking the input port of the module to connect to. You can also connect between a pipe and a module by first clicking the pipe, and then clicking the input port on the connecting module.

A pipe is default color-coded according to the type of data that it transmits. Some examples are listed below.

Table 6.1: Pipe Colors and Data Types

Green Rasters

White Vectors

Red Bitmaps

Blue Pseudocolor tables

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Tip

The most frequent causes for dead pipes are moving, renaming, and deleting an input file or layer.

Tip

Always connect modules before you configure them, because for some modules the default settings of an input layer can override a module’s configuration. An exception is the IMPORT module, which you must configure for it to display a port.

To connect CDLAND7 and AUTOGCP:

1. Click the raster output port on CDLAND7 and then click the input port on the SPLIT module.

A green pipe connects these two modules.

2. Click the second output port on the SPLIT module and then click the input raster port on SATMODEL.

This is a temporary connection that allows you to access the third channel from the raw Landsat image.

3. Click the third output port on the SPLIT module and connect to the Input Image Layer port on AUTOGCP.

4. Click the pipe containing channel 2 of the raw Landsat image.

The pipe displays with a blue border.

5. Press the DELETE key.

6. Click the Satellite Orbital Ephemeris Layer port on the CDLAND7 module and connect this to the Orbit Layer port on the AUTOGCP module.

A blue pipe connects these two modules.

Note

A SPLIT module will split a 'Fat' pipe into a 'Thin' pipe. Since a 'fat' pipe contains many layers, while a 'thin' pipe contains only 1 layer, this module will be transferring 1 layer from a 'fat' pipe and placing it into its own 'thin' pipe. This is useful if only a particular layer needs to pass on to another module.

Yellow Look-up tables

Cyan Binary

Black Dead pipe

Table 6.1: Pipe Colors and Data Types

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To connect the IMPORT modules to AUTOGCP:

1. Click the raster port on the IMPORT module below the CDLAND7 module and connect this to the Reference Image Layer raster port on the AUTOGCP module.

2. Click the raster port of the lower IMPORT module and connect this to the Elevation Layer port on AUTOGCP.

Figure 6-7: CDLAND7 and IMPORT pipe connections to AUTOGCP

To connect AUTOGCP and GCPREFN:

1. Connect the GCP Layer port on AUTOGCP to the GCP Layer to be Refined port on GCPREFN.

A cyan pipe connects these modules.

2. Connect the orbit port on CDLAND7 to the orbit port on GCPREFN.

To set up the connections for SATMODEL:

1. Connect the output GCP port on GCPREFN to the GCP port on SATMODEL.

2. Connect the output raster port on CDLAND7 to the raster input port on SATMODEL.

3. Connect the output orbit layer port on CDLAND7 to the orbit layer port on SATMODEL.

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Figure 6-8: Pipe connections to SATMODEL module

To set up the connections for SATMODEL and ORTHO:

1. Connect the output raster port on CDLAND7 to the Image Layers to be Processed raster port on ORTHO.

2. Connect the output raster port from the second IMPORT to the Elevation Layer raster port on ORTHO.

3. Connect the Output Math Model Layer port on SATMODEL to the Math Model Layer port on ORTHO.

4. Connect the raster port on ORTHO to the Any port on EXPORT.

Figure 6-9: Model to orthorectify a Landsat-7 image

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To configure the AUTOGCP module:

1. Double-click the AUTOGCP module.

2. For the Sampling Count, enter a value of 50.

This is the number of GCPs to be matched.

3. For the No Data Image Value, enter 0.

The defaults for the remaining parameters will be used.

4. Click Accept.

To configure the GCPREFN:

1. Double-click the GCPREFN module.

2. For the Rejection Method, select RMS Error (5) from the dropdown list.

3. Click Accept.

For this model, there are no parameters to configure for the SATMODEL module. The Output Projection for the ortho images will be set in the ORTHO MCP.

To configure the ORTHO module:

1. Double-click the ORTHO module.

2. On the Input Parms 1 tab, enter UTM 10 U E012 for the Output Projection.

3. For the Output Pixel Ground Size: X, Y, enter 30, 30.

4. Set the Resample Mode to Cubic.

5. Click Accept.

To configure the EXPORT module:

1. Double-click the EXPORT module.


3. For the File name, enter ortho.pix and click Save.

4. Select the Overwrite Existing File option.

If the model is run more than once, ortho.pix will be overwritten.

5. Click Accept.

Tip

The COMMENT module allows you to enter comments relevant to the particular model.

To add comments:

1. Add a COMMENT module to the canvas and place it above the CDLAND7 module.

2. Double-click the COMMENT module.

3. On the Input Params 1 tab, enter Read Landsat Imagery from HDF file.

4. Click Accept.

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5. Resize the comment box so all the text is visible.

Figure 6-10: Model with comment

6. Add any other comments you would like to the model.

You are now ready to execute your model.

To execute a model:

• From the Execute menu, select Run or click the Run button on the toolbar.

The status indicator bars on the modules show the progress of each operation as it is executed. Another progress indicator in the display area of the Modeler window monitors the progress of the entire model.

It will take a few minutes for the model to run to completion.

When the execution of the model has completed, double-click the AUTOGCP and GCPREFN modules and examine the reports in the LOG tab.

To see how well the ortho image is registered to geo_landsat.pix, open the files in Focus.

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Saving a Model Now that you have created your first model with Modeler, you will save the model as a MOD file.

To save your model:

1. From the File menu, select Save Model.


2. Navigate to the LANDSAT folder.

3. In the File Name box, type ortho_landsat.mod.

4. Click Save.

The File Selector window closes and your model is saved as a MOD file.

5. From the File menu, click Close Model.

Note

When you save a model after it has executed, the intermediate and output files are not saved with the model.

In this lesson you:

• Launched Modeler and added modules to the canvas

• Connected the modules in the model

• Configured the modules in the model

• Ran the model in single execution model

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In this lesson you will:• Add modules to the canvas

• Configure the modules for batch execution


• Run the model in batch execution model

OrthoEngine projects can now be created and saved in Modeler. This allows you

to set up a work flow in Modeler to perform some of the image processing of your

data. You can then continue working in OrthoEngine with the exported project file.

The model for this lesson uses a total of seven modules from the Module Librarian. First, you will batch import two ASTER images. An OrthoEngine project file will be created for the data and tie points will be automatically collected and refined. The project file will be saved once the tie points have been collected and could later be used in OrthoEngine for further data extraction. CPMMSEG will apply the sensor model and the images will be orthorectified with ORTHO.

To start setting up the model:



• 1 ACCUMULATE module

• 1 CRPROJ module

• 1 AUTOTIE module

• 1 TPREFN module

• 1 CPMMSEG

• 2 RELEASE modules

• 1 ORTHO module

• 2 EXPORT modules


Figure 6-11: Modules on canvas with no pipe connections

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To configure the first IMPORT module:

1. Double-click the first IMPORT module.


2. Click Browse and navigate to the ASTER_MODELER folder.

3. Select 3b.pix and click Open.

4. From the Available Layers list, select the raster layer.

5. Click Batch.

The Module Control Panel expands.

6. Click the + button.

7. Select the second batch parameter set in the list.

8. Right-click on the file column heading and select Add Files.... Select the 3n.pix file.

Figure 6-12: IMPORT Module Control Panel with Batch parameter sets

9. Click Accept.

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1. Double-click the IMPORT module above the ORTHO module.


3. Select aster_dem.pix and click Open.

4. From the Available Layers list, select the 32R SRTM DEM layer.

Figure 6-13: IMPORT Module Control Panel

5. Click Accept.

Note

The pseudo GCPs delivered with ASTER data cannot be imported into the model because the 3N and 3B images have different GCPs with the same GCP ID.

The modules will now be connected with pipes.

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To connect the IMPORT and CRPROJ modules:

1. Click the raster port on the IMPORT module and connect with the input port of the upper ACCUMULATE module.

2. Connect the output port on the ACCUMULATE module to the raster port on the CRPROJ module.

Note

The ACCUMULATE module accumulates all incoming layers during batch execution. These layers are released when all batch runs have been exhausted and all modules executed.

Figure 6-14: IMPORT module connected to CRPROJ

To connect CRPROJ to CPMMSEG and EXPORT:

1. Connect the Output OrthoEngine Project port on CRPROJ to the Input OrthoEngine Project port on AUTOTIE.

2. Connect the Output OrthoEngine Project port on AUTOTIE to the Input OrthoEngine Project port on TPREFN.

3. Connect the Output OrthoEngine Project port on TPREFN to the Input OrthoEngine Project port on the first EXPORT module.

4. Connect the Output OrthoEngine Project port on TPREFN to the Input OrthoEngine Project port on CPMMSEG.

5. Connect the Output raster port from the ACCUMULATE module to the Input raster port on the first RELEASE module.

6. Connect the Output raster port from the first RELEASE module to the Input raster port on the CPMMSEG module.

Note

The RELEASE module releases all incoming layers in sequence by layer or by group during batch execution. Release takes place during each model execution until all groups or layers have been released.

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Figure 6-15: Connections from CRPROJ

To connect ORTHO and EXPORT:

1. Click the output raster port from the ACCUMULATE module and click the input port on the second RELEASE module.

2. On the second RELEASE module click the output port and connect it to the input Image Layers to be Processed raster port on the ORTHO module.

3. Connect the output Math Model Layer port on CPMMSEG to the input Math Model port on the ORTHO module.

4. Connect the output Raster port of the second IMPORT module to the input Elevation Layer port of the ORTHO module.

5. Connect the output Raster port on ORTHO to the input port on the EXPORT module.

Figure 6-16: Model showing all pipe connections

Now that all of the modules are connected, you will now configure them.

To configure the CRPROJ module:

1. Double-click the CRPROJ module.

This module creates an OrthoEngine Project file.

2. For the Output Projection, enter UTM 34 T E012.

3. From the Model Type list, select SAT (Toutin’s Model).

4. Click Accept.

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To configure the AUTOTIE module:

1. Double-click the AUTOTIE module.

This module performs automatic tie point collection from a pair of overlapping images.

2. On the Input Params 1 tab, set Number of Points per Area to 60.

3. For Distribution, select Overlap.

4. For the Approximate Elevation, enter 1300.

5. For the Search Radius, enter 200 and click Accept.

To configure the TPREFN module:

1. Double-click the TPREFN module.

This module automatically refines tie points by eliminating those with large residual errors.

2. For the Rejection Criteria, enter 5, 10, 10.

Mode 5 (RMS Error rejection) criteria will be used. Tie points with an X or Y RMS greater than 10 pixels will be rejected.

3. Click Accept.

To configure the first EXPORT module:

1. Double-click the EXPORT module below the CPMMSEG module.


3. For the File name, enter aster_tp.prj and click Save.

This will export the OrthoEngine project file.

Note

CPMMSEG computes and copies the math model contained in a project file to a Math Model layer in the input file. There are no parameters to configure for this module.


1. Double-click the ORTHO module:

ORTHO orthorectifies a raw image based on the math model computed by CPMMSEG.

2. For the Output Projection, enter UTM 34 T E012.

3. For Output Pixel Ground Size: X, Y, enter 15, 15.

4. Click Accept.

To set the batch parameters for the EXPORT module:

1. Double-click the EXPORT module to the right of the ORTHO module.

2. Click Batch.

3. In the Batch parameter sets table, right-click the File column heading, and choose From Input Module.

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4. Click OK.

The Batch parameter sets table updates with the names of the input files.

5. Press the SHIFT key, and select both parameter sets.

6. In the Batch parameter sets table, right-click the File column heading, and choose Add Prefix/Suffix.

The Add Prefix/Suffix window opens.

7. In the Add Prefix/Suffix window, click the Prefix Text check box and enter ortho_.

This option applies to both selected cells.

Figure 6-17: Add Prefix/Suffix window

8. Click OK.

The File names update in the Batch parameter sets table.

9. Click Accept.

To execute the model in batch mode:

• From the Execute menu, select Run Batch or click the Run Batch button on the toolbar.


When the model has finished executing, double-click the Log tab of the TPREFN MCP. You will see a report of the deleted tie points, the original number of tie points as well as RMS errors. The output ortho images can be viewed in Focus.

To save the model:



2. Navigate to the ASTER_MODELER folder.

3. In the File Name box, type autotie_aster.mod and click Save.


In this lesson you:

• Added modules to the canvas

• Configured the modules for batch execution


• Ran the model in batch execution model

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• Add modules to the canvas


• Configure the modules for batch execution

• Run the model in batch execution mode

Entire work flows can be set up within OrthoEngine to perform automatic image-to-

image registration, calculate the sensor model, orthorectify imagery and perform

automatic mosaicking.

The model for this lesson uses a total of eight modules from the Module Librarian.

First, you will batch import two SPOT scenes. Two other IMPORT modules will be

used for a geocoded image and a DEM. GCPs will be automatically collected from

the geocoded image with AUTOGCP and then refined with GCPREFN. The

satellite model will be applied and the images will be orthorectified with ORTHO.

The two orthorectified images will be automatically mosaicked with AUTOMOS.

The individual ortho images and the mosaicked image will be exported.

To start setting up the model:



• 1 AUTOGCP module

• 1 GCPREFN module

• 1 SATMODEL

• 1 ORTHO module

• 1 ACCUMULATE module

• 1 AUTOMOS module

• 2 EXPORT modules


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Figure 6-18: Modules on canvas with no pipe connections

To setup the Batch Parameter Sets for the first IMPORT module:

1. Double-click the upper IMPORT module.

2. Click Browse and navigate to the SPOT folder.

3. Select SPOTLEFT.PIX and click Open.

4. From the list of Available Layers, select the 8U raster layer and the orbital segment.

5. Click Batch.

6. Click the + sign.

7. Select the second Batch parameter set.

8. Browse to the SPOT folder and select SPOTRIGHT.PIX.

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Figure 6-19: Batch IMPORT of raw SPOT data

9. Click Accept.


1. Double-click the middle IMPORT module.


3. Select SPOT_MOSAIC.PIX and click Open.

4. Select the 8U raster layer.

5. Click Accept.

To configure the third IMPORT module:

1. Double-click the lower IMPORT module.


3. Select SPOTDEM.PIX and click Open.

4. Select the 16S raster layer.

5. Click Accept.

Now that the IMPORT modules are configured, you will set up the pipes to connect the modules in the model.

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To connect the IMPORT modules to the AUTOGCP module:

1. Click the OutputRaster1 port on the upper IMPORT module, then click the Input Image Layer port on the AUTOGCP module.

2. Click the OutputORB1 port on the upper IMPORT module, then click the Orbit Layer input port on the AUTOGCP module.

3. Click the OutputRaster1 port on the middle IMPORT module, then click the Reference Image Layer port on the AUTOGCP module.

4. Click the OutputRaster1 port on the lower IMPORT module, then click the Elevation Layer input port on the AUTOGCP module.

Figure 6-20: Connections to AUTOGCP

To connect GCPREFN and SATMODEL:

1. Connect the GCP ports on the AUTOGCP and GCPREFN modules.

2. Connect the Orbital ports on the upper IMPORT module to the GCPREFN module.

3. Connect the GCP ports on the GCPREFN and SATMODEL modules.

4. Click the OutputRaster1 port on the upper IMPORT module, then click the Image Layers to be Processed port on the SATMODEL module.

5. Click the OutputORB1 port on the upper IMPORT module, then click the Orbit Layer port on the SATMODEL module.

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Figure 6-21: Connections to GCPREFN and SATMODEL

To connect ORTHO and ACCUMULATE:

1. Connect the raster layer port on the upper IMPORT module to the Image Layers to be Processed port on ORTHO.

2. Connect the raster layer port on the lower IMPORT module to the Elevation Layer port on ORTHO.

3. Connect the Math Model ports on SATMODEL and ORTHO.

4. Connect the Output Raster Layer port on ORTHO to the input port on the ACCUMULATE module.

To connect EXPORT and AUTOMOS:

1. Connect the output raster port from ORTHO to the first EXPORT module.

2. Connect the output port on the ACCUMULATE module to the Image Layers to be Processed port on AUTOMOS.

3. Connect the Output Mosaic Layers port on the AUTOMOS module to the second EXPORT module.

The AUTOGCP module automatically registers a raw image to a geocoded image and creates a GCP segment stored in the raw image file. You can open the AUTOGCP Module Control Panel to see the parameters, however in this lesson, you will use the default parameters.

Tip

You can rotate the ports on a module by clicking the Rotate button on the Modeler toolbar. This is useful to help organize your pipes.

You will now configure the remaining modules.

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To configure the GCPREFN module:

1. Double-click the GCPREFN module.

This module refines GCPs in a GCP segment and removes points with large errors.

2. Ensure the Model Type SAT (Toutin’s Model) is selected.

3. For the Rejection Method, select Absolute Distance (4) from the dropdown.

Mode 4 (absolute distance rejection) will be used. GCPs with residual values greater than 1 pixel X and 1 pixel Y will be rejected.

4. Click Accept.


1. Double-click the ORTHO module.

ORTHO orthorectifies a raw image based on the math model computed by SATMODEL.

2. For the Output Projection, enter UTM 11 E000.

3. For the Output Pixel Ground Size: X, Y, enter 10, 10.

The remaining default parameters will used.

4. Click Accept.

To configure the AUTOMOS module:

1. Double-click the AUTOMOS module.

This module performs automatic mosaicking of a set of geocoded images.

2. For the Color Balancing Method, select OVERLAP.

3. For the Cutline Generation Method, select EDGE.

4. Click Accept.

To set the batch parameters for the first EXPORT module:

1. Double-click the EXPORT module to the right of the ORTHO module.

2. Click Batch.

3. In the Batch parameter sets table, right-click the File column heading, and choose From Input Module.

The From Input Module window opens. If your model contains more than one IMPORT module, you would select from which module to select the input file names.

4. Click OK.

The Batch parameter sets table updates with the names of the input files.

5. Press the SHIFT key and select both parameter sets.

6. In the Batch parameter sets table, right-click the File column heading, and choose Add Prefix/Suffix.

The Add Prefix/Suffix window opens.

7. In the Add Prefix/Suffix window, click the Prefix Text check box and enter ortho_.

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This option applies to both selected cells.

8. Click OK.

The File names update in the Batch parameter sets table.

9. Click Accept.

To configure the second EXPORT module:

1. Double-click the EXPORT module below the AUTOMOS module.


3. Enter the file name auto_mosaic.pix and click Save.

4. Click Accept.

The AUTOGCP and SATMODEL modules do not need to be configured as default parameters will be used.

Figure 6-22: SPOT model

You are now ready to execute your model.

To execute the model:

• From the Execute menu, select Run Batch or click the Run Batch button on the toolbar.


To save the model:



2. Navigate to the SPOT folder.

3. In the File Name box, type spot_autogcp.mod and click Save.


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In this lesson you:

• Added modules to the canvas


• Configured the modules for batch execution

• Ran the model in batch execution model

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Minimum GCP requirements

The following table lists the minimum number of ground control points (GCPs)

to collect, but it is recommend that you collect more than the minimum to

ensure accuracy. However, collecting over 20 GCPs per image does not

significantly improve the accuracy for most math models. To improve the

accuracy, collect GCPs evenly throughout the image at a variety of elevations

and in areas where images overlap. Also, the quality of the GCPs impacts the

number needed to ensure accuracy.

Table A.1: Minimum Number of GCPs

Math Model / Data Minimum GCPs Recommended

Aerial Photography3 to 4 per project

3 per image for highest accuracy

Aerial Photography with GPS/INS GCPs optional

Satellite Orbital:

SPOT 1 to 4 4 per image 6 to 10 per image

ASTER, AVNIR-2, CARTOSAT, CBERS, DMC, EOC, IRS, LANDSAT, ORBVIEW, PRISM, QUICKBIRD Basic, SPOT 5

6 per image 10 to 15 per image

ASAR, EROS, ERS, FORMOSAT-2, IKONOS, JERS, PALSAR, QUICKBIRD Ortho Ready, RADARSAT

8 per image 10 to 15 per image

ASAR/PALSAR/RADARSAT Specific Model GCPs optionalImprove accuracy with a minimum of 8 GCPs for RADARSAT only

Rational Functions:

If Computed from GCPs 5 per image * 19 per image *

If Extracted from Image File GCPs optional

For IKONOS Ortho Kit, improve accuracy with 1 or more GCPs (using zero-order RPC adjustment); for CARTOSAT and QUICKBIRD Ortho Ready, minimum of 3 GCPs (using first-order RPC adjustment); for ORBVIEW, minimum of 1 GCP (using zero-order RPC adjustment)

Thin Plate Spline 3 per imageCollecting more than the minimum will average out errors introduced by inaccurate GCPs or terrain variations

Appendix

A

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Geomatica OrthoEngine - AppendixA: Minimum GCP requirements

* Depending on the number of coefficients that you want to use.

Polynomial:

First-order 4 per image

Collecting more than the minimum will average out errors introduced by inaccurate GCPs

Second-order 7 per image

Third-order 11 per image

Fourth-order 16 per image

Fifth-order 22 per image

Table A.1: Minimum Number of GCPs

Math Model / Data Minimum GCPs Recommended

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orthoengine training manual

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