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Topic 5. Stereophotogrammetric

Measurements

Division of Geodetic Engineering College of Engineering and Information Technology Caraga State University

GE 119 – PHOTOGRAMMETRY 2

Instructor: Engr. Jojene R. Santillan [email protected] [email protected]

Lecture Notes in GE 119: Photogrammetry 2 TOPIC 5. STEREOPHOTOGRAMMETRIC MEASUREMENTS 2

Outline

• Measurement of three-dimensional (3D) object coordinates from stereopairs

• Creation of Digital Surface Model from Stereopair via Image Matching

• Orthoimage Generation


Intended Learning Outcomes

• After this lecture and hands-on exercises, the students must have: – Understood the concepts behind

stereophotogrammetric measurements

– Explained the procedures involved in stereophotogrammetric measurements particularly on 3D object coordinate measurements, DSM and DTM creation, orthorectification, and 3D data collection

– Performed measurement of 3D object coordinates using photogrammetric software and datasets

– Created DSM from a stereopair using photogrammetric software and datasets

– Generated orthoimage using photogrammetric software

– Conducted simple 3D data collection using photogrammetric software and datasets

4 Lecture Notes in GE 119: Photogrammetry 2 TOPIC 5. STEREOPHOTOGRAMMETRIC MEASUREMENTS

MEASUREMENT OF THREE-DIMENSIONAL (3D) OBJECT COORDINATES FROM STEREOPAIRS


3D Object Coordinates Measurement from Stereopairs

• After stereopair orientation, 3D object measurement can now be done

– E.g., we can get X, Y, Z coordinates of any point within the stereogram

• In digital photogrammetry, 3D object measurement is called feature collection


3D Measurement Workflow

Interior Orientation

• Conduct interior orientation of each photo

Exterior Orientation

• Conduct exterior orientation of each photo

Stereopair orientation

• Orient the stereopair in different ways:

• Independent relative

• Dependent relative

• Direct

• Absolute

3D Measurements


3D Measurement Demonstration Using LISA FOTO


Datasets Used:

• Scanned Aerial Photos: • Scanning Resolution: 300 dots per inch (dpi) or 84.7 μm

– 157.IMA - 158.IMA


Some Notes on Scanning Resolution

• Aerial photographs converted into digital images are usually scanned using flatbed scanners

• For photogrammetry, scanning resolution of 300 to 600 dpi maybe used

• DPI = dots per inch the ―dot” = the “pixel”

• The scanning resolution is equivalent to the scanned photo (or digital image)‘s geometric resolution

• Geometric resolution = “pixel size”

• If you scan an aerial photo with the resolution in dpi set in the scanner, it can be converted into the metric system using the following equations:


Relationship between scanning resolution, pixel size and photo scale


Step 1. Interior Orientation

• Refer to your Exercise 1 for the procedures

• Needed inputs: – Project Definition:

• X range from 1137300 to 1140000 • Y range from 969900 to 971700 • Z range from 1000 to 1700 • Pixel size: 5 • Length unit: m (meters)

– Camera definition: • Focal length: 152.9 mm • Camera-internal coordinates of fiducial

marks (in mm)

Fiducial No. x value y value

1 113.000 0.000

2 0.000 -113.000

3 -113.000 0.000

4 0.000 113.000


Interior Orientation Results (1) • Interior Orientation Errors/Residuals (example for for image

157.IMA ):

• For real life applications, residuals, including the Standard Deviation, must be not more than ½ of the scanning resolution (or the pixel size) – Since the scanned photos have a scanning resolution of 300 dpi or 84.7

μm, the maximum residual must be ½ of 84 μm = 42.35 μm or 0.04235 mm

– If residuals or standard deviation > ½ of pixel size, repeat the interior orientation


Interior Orientation Results (2)

• Output files generated: 157.INN and 158.INN (files containing the interior orientation parameters including the affine transformation coefficients)


Step 2. Exterior Orientation

• Refer to your Exercise 2 for the procedures

• Needed inputs:

– Interior orientation output files (157.INN and 158.INN)

– Ground Control Points and their corresponding location in the two photos

• See next slides


GCP Data


GCP Locations in 157.IMA


GCP Locations in 158.IMA


Exterior Orientation Results (1)

• Example Exterior Orientation Errors/Residuals (for image 157.IMA):

Again, for real-life applications/photogrammetry projects, residuals and standard deviation must not exceed half the pixel size.



• Example Output File Generated (157.ABS) containing the Exterior Orientation parameter values (i.e., the variables of the collinearity equations):

Focal length

Rotation angles (φ, ω, К ) in radians`

Exposure station location (XL, YL, ZL)



• Each photo subjected to exterior orientation will have its own sets of exterior orientation parameter values

• If you know the 3D coordinates of an object on the earth‘s surface covered by the photo, you can locate that object in the photo using the collinearity equations this is the essence of Exterior Orientation

Note: xp1, yp1, xp2 and yp2 are pixel coordinates and maybe written as X’p1, Y’p1, X’p2 and Y’p2 to be consistent with our notations in previous lectures.


Step 3. Stereopair Orientation

• Needed inputs: – Exterior orientation parameter values for each

photo

– Collinearity equations set-up using the exterior orientation parameter values which can related to each other to determine the 3D coordinates of any point in the overlapping areas.

– GCP 3D coordinates (for both photos)

• After the orientation of the two photos the complete geometry during photo acquisition is reconstructed


• Recall Topic 4: The 4 collinearity equations can be related to each other to determine the value of (XP, YP, ZP)!

Note: xp1, yp1, xp2 and yp2 are pixel coordinates and maybe written as X’p1, Y’p1, X’p2 and Y’p2 to be consistent with our notations in previous lectures.


Recall Topic 4: Relating the 4 equations depends on the coordinate system to be used

• The values of xp1, yp1, xp2 and yp2 and the 3D ground coordinates can be referenced to a single coordinate system

• The coordinate system is called the ―model coordinate system“ • Note: xp1, yp1, xp2 and yp2 are pixel coordinates and maybe written as

X’p1, Y’p1, X’p2 and Y’p2 to be consistent with our notations in previous lectures.


Stereopair Orientation in LISA (1)

• In LISA Software, the two photos are ―connected‖ to form a model – i.e., the connection is made possible by the collinearity

equations of each photo

• ―Direct orientation‖ is used by LISA software – the model coordinate system becomes identical with the

ground coordinate system



• The GCP data is used by LISA as a test to check if a GCP is represented in both photos



• In LISA, stereopair orientation also results to getting the following:

• Mean pixel size of the photos in terrain units (m)

• Average scale of the photos (S)

• Maximum attainable accuracy in elevation

– The above are computed automatically in LISA FOTO Software


Getting the mean pixel size • Using the GCP data, the

distances between different points in ground (X, Y) and pixel coordinates (X‘, Y‘) are compared

– Pixel coordinates (X‘, Y‘) are computed using the collinearity equations

• For each pair of point, we get their ground distances and pixel distances and their ratios (i.e., ground distance/pixel distance)

• For all pairs, we can get the average ratio of the ground distance with the pixel distance

– Note: Pixel distance = number of pixels

• The average ratio = mean pixel size

• Assume: pixel is a square

+ L157

GCP 1.

GCP 2.

X and X‘

Y and Y‘

Distance between GCP 1 and 2, in meters = square root of the sum of the squares of X and Y

Distance between GCP 1 and 2, in pixels = square root of the sum of the squares of X‘ and Y‘


Getting the average scale • The average scale (for the two photos) is

determined by getting the ratio of the scanning resolution or geometric resolution (or the pixel size in mm) with the mean pixel size in m

• i.e.,

• Scale = (geometric resolution/mean pixel size)

• Note: The equation S = f/H‘ is not applicable since the photos are not vertical. – One can check if the photos are vertical if the values

of the rotation angles (φ, ω, К ) after exterior orientation are all zeros.


Getting the air base and flying height • The airbase (B) is

computed using the (X,Y) ground coordinates of the two exposure stations – B is horizontal distance

• The average flying

height above datum (H) is computed by averaging the Z values of the two exposure stations

• The flying height above terrain (H’) can be computed by subtracting from H the mean terrain height


Maximum Attainable Accuracy in Elevation (Z) Values that can be obtained from the Oriented Stereopair

• Maximum attainable accuracy (MAC)= Mean Pixel Size * (Flying Height Above Terrain/Air Base)

• i.e., MACZ = Mean Pixel Size * (H’/B)

• In digital photogrammetry, MAC greatly depends on scanning resolution. – The higher the resolution, the smaller the value of MACz

since the mean pixel size will also be smaller.

• In analog photogrammetry, the accuracy in Z can reach a value of 0.1 per thousand (0.1/1000) of the flying height above terrain


Example

• A stereopair consisting of Photos 157 and 158 were subjected to stereopair orientation using LISA software.

• Note that each photo was scanned at a resolution of 300 dpi

• Each photo underwent interior and exterior orientations. • After the exterior orientations, the 3D ground coordinates

of the exposure stations were determined as follows:

– L157: (1138648.425, 970300.072, 3152.827) – L158: (1138635.349, 971264.050, 3155.867)

• The 3D coordinates of 12 GCPs were used as inputs into the collinearity equations to determine their corresponding pixel coordinates. The computed average ratio between the ground distances and pixel distances is 1500 m : 1340 pixels

• Determine the following: a. The average pixel size, in m b. The average scale of the scanned photos c. The maximum attainable accuracy of elevation values


Solution

a. Mean pixel size = 1500 m / 1340 pixels

= 1.12 m/pixel

1.12 m

1.12 m


• b. Average scale = (geometric resolution / mean pixel size)

Geometric resolution = 300 dpi = 84.7 um = 84.7 x 10-6 m

Mean pixel size = 1.12 m

Scale = 84.7 x 10-6 m / 1.12 m

Scale = 1:13,223


c. MACZ = Mean pixel size * (H‘/B)

H‘ = [(ZL157 + ZL158)/2] – h

H‘ = [(3152.827 m + 3155.867 m) / 2] – 1100m

H‘ = 3,154.347 – 1100 m = 2,054.347 m

B = (XL157 – XL158)2 + (YL157 –

YL158)2

B = (1138648.425 – 1138635.349)2 + (970300.072 − 971264.050)2

B = 964.067 m

MACZ = 1.12 m * (2054.347m/964.067m)

MACZ = 2.387 m this means that any elevation measured

from the stereopair can have an error of at least 2.387 m


Step 4: 3D measurements using LISA Software

• After stereopair orientation, 3D object measurement can now be done

– E.g., we can get X, Y, Z coordinates of any point within the stereogram

• In digital photogrammetry, 3D object measurement is called feature collection


3D Measurement Demo Using LISA FOTO


Some observations when doing Manual 3D Measurement Using LISA Software

• An initial elevation value is set which is the same as the mean of the elevation range in the project area (from the project definition file)

• The left and right photos are displayed; a cross-hair (―measuring mark‖) is present in each photo

• Viewing may be done for both photos side by side or using Anaglyph 3D method


Some observations when doing Manual 3D Measurement Using LISA Software (2) • Initially, the positions ―under‖ the measuring marks

for the left and right photos are not exactly identical

• To be able to measure 3D coordinates of an object, the user must move (in x, y and z directions) the stereopair such that the two cross-hairs are pointing to the same exact location of the object in each photo – i.e., you must tell LISA FOTO the location of the object in

each photo!

– As the user moves the stereopair, LISA FOTO automatically do the ―space intersection‖ using the collinearity equations and calculates the 3D coordinates

• The 3D coordinates are displayed in the status bar


Principle Behind 3D Measurement from Stereopairs in LISA FOTO

Lleft photo Lright photo

Air Base

Epipolar plane

PXYZ

P‘ P‘‘

Changing the elevation in point P (on the surface) leads to a linear motion (left – right) of the points P‘ and P‘‘ within the photos along the epipolar lines.

Terrain surface


Some 3D Measurements Applications

• Cartography and GIS – Digitizing roads, rivers, buildings and similar

objects • Using individual aerial photos or satellite images, 2D

information can only be extracted (no Z values)

• But with stereopairs, for every point, complete 3D information can be extracted

• Terrain Modeling – Since 3D coordinates of every point in the

overlapping areas (stereogram) can be extracted, it is possible to generate terrain models such as: • DTM = Digital Terrain Model

• DSM = Digital Surface Model


Some problems with manual 3D measurement

• If we wish to get the 3D coordinates of several hundreds of points, manual measurement would be time-consuming and tiresome!


Is there an easier way to collect 3D coordinates?

• There must be an ―automatic‖ way where the software will no longer ask the user to locate on the right and left photos the corresponding positions of the point of interest

• If we can find an algorithm telling the computer what we mean by ―corresponding positions‖, then the software should be able to do the 3D measurements/3D collection automatically over the whole model area forming a DSM


• Fortunately, yes! There is an automatic way!

• We can form a DSM automatically from the stereopair by utilizing what we call an ―Image Matching‖ algorithm


CREATION OF DIGITAL SURFACE MODEL VIA IMAGE MATCHING


Brief background about DTMs/DSMs

• DTMs and DSMs are commonly called Digital Elevation Models (DEMs)

• They are 3D representations of the earth surface

– The earth surface is represented in (X, Y and Z)

– The difference between DTM and DSM is based on what the Z value represents


DSM vs. DTM


DSM vs. DTM

The actual earth surface (real world)

DSM (The Z value represents the top elevation of objects on the earth surface)

DTM (The Z value represents the elevation of the ground, i.e., we remove the trees, vegetation, and buildings)


DSM vs. DTM

• DSM = a 3D representation of the Earth‘s surface where all features including man-made structures and vegetation are present.

• DTM = a 3D representation of the Earth‘s surface where all features including man-made structures and vegetation have been removed

– also called a ―bare earth DEM‖


“DSM”


“DTM”


How can we create a DSM from a stereopair?

• A DSM can be generated by extracting 3D coordinates of every point (or pixel) in the model area

• If we are using LISA FOTO, that means we need to tell the software the corresponding positions of every point (i.e., every pixel) in the model area on the left and right photos

• If we are able to get the corresponding positions, then LISA FOTO will calculate the 3D coordinates – We can then obtain a grid of points with 3D coordinates!

• From the 3D coordinates, we can generate a DSM by

converting the grid of points into a continuous elevation surface


Grid of Points with 3D Information


DSM Generated from the Grid of Points


• But the problem: it is difficult and time consuming to manually tell the software the corresponding positions of all pixels on the left and right photos!


Image Matching

• An automatic approach of finding the corresponding positions of an object in two photos or images

Through image matching, it would be possible to determine the position of a pixel on the left photo to its corresponding position on the right photo


Image Matching

• Area-based matching (ABM) – A commonly used type of Image Matching (the one

used in LISA FOTO)

– ABM does the following: • compares the neighborhood of a point in the left image

(called the ―reference matrix‖) with the neighborhood of the approximate position in the right image (called the search matrix)

• The comparison is iterative: the search matrix is allowed to move for a given number of pixels left-right and up-down

• In any position a value is calculated giving a measure of correspondence of the search matrix to the reference matrix

– i.e., a measure which can indicate the degree of similarity of the search matrix with the reference matrix

– Commonly used measure: correlation coefficient


ABM: the aim is to find the same pixel at the center of the reference matrix on the right image

Left Photo/Image Right Photo/Image

Reference matrix (in fixed position until the min. correlation coefficient is obtained, then moves to the next pixel)

Search matrix (can move left-right, up-down until the minimum correlation coefficient is obtained)


Area-based Matching

• The correlation coefficient ranges from 0 meaning that both the reference and search matrices are completely different, and 1 meaning that the two matrices are identical

• The user must define the size (dimension in terms of rows and columns) of the reference and search matrices

• The user must also set the minimum value of the correlation coefficient which will tell the ABM algorithm to stop searching at this instance, the pixel at the center of the search matrix is

considered as the same pixel which is at the center of the reference matrix

The algorithm will proceed to finding the next pixel (i.e., the reference matrix will move to the next pixel) The procedure is repeated


How is the correlation coefficient computed?

• The DN values in the search matrix is compared to the DN values in the reference matrix

• The correlation coefficient (‗r‘) is then calculated based on the pair of DN values

COMPARE THE DN VALUES OF THE CORRESPONDING

ELEMENTS OF BOTH MATRICES


Things to Consider When Using ABM

• ABM‘s performance can be affected by:

– brightness and contrasts conditions

– Relief

• Neighborhoods of corresponding points in neighboring images are normally not completely identical the

correlation coefficient will never reach the value of 1

• Areas with good contrast and flat terrain, correlation coefficient of greater than 0.9 is possible

• Areas with low contrast and/or strong relief like high mountainous regions corelation coefficient will not be

higher than 0.3 even in exactly corresponding locations


DSM Generation Via Image Matching Using LISA FOTO


Important:

• In Linder (2016), pages 44-56, the ―DTM‖ generated via image matching is actually not a DTM but a DSM!

– The author took a note of this in page 56 (Section 4.7.4)

– The procedures for actual DTM generation from the DSM are explained in Section 4.7.4 (pages 56-57)


DTM Generation from DSM

• A DTM can be generated by applying ―filtering‖ algorithms to a DSM

– The idea is to remove surface features (e.g., vegetation, buildings, etc.) from the DSM

– One common approach is applying a mean filter

– The mean filter will remove peaks and smoothen the DSM which will result to a semi-realistic DTM

• Semi-realistic since not all peaks and surface features are removed from the DSM


Generating Contours from “DTM”

• Using LISA BASIC, contours can be generated from the DTM

• One can set the contour interval (e.g., 10-m)

Linder, W., 2016. Digital Photogrammetry – A Practical Course, Springer-Verlag Berlin Heidelberg, Germany


Image overlaid with contours



ORTHOIMAGE GENERATION


Orthophoto, orthoimage

• An orthophoto, orthophotograph or orthoimage is an aerial photograph or image geometrically corrected ("orthorectified") such that the scale is uniform: the photo has the same lack of distortion as a map.

https://en.wikipedia.org/wiki/Orthophoto#/media/File:Ortofoto_Citt%C3%A0_Alta,_Rocca.jpg


Why is orthorectification necessary?

• Aerial photographs or images have geometric problems resulting from:

– Natural or artificial relief

– Central perspective projection

• The above lead to radial-symmetric displacement of objects in the photo or image!

• The geometric problems cannot allow for the accurate direct measurement of distances, angles, and areas (i.e. mensuration) from the photo or images!


Illustration

• Very steep slopes, may they be natural or artificial (walls of houses etc.), will lead to hidden areas in the image.

• This effect increases with

stronger relief, greater lens angle and greater distance from the image center—the effect is not only a hidden area in the image

• Objects situated in or close to the terrain nadir point will have no or nearly no displacement in the image.



Distortions in an aerial photo

Source: http://elte.prompt.hu/sites/default/files/tananyagok/MapGridsAndDatums/ch09.html


Orthorectification/Orthoimage Generation

• Orthorectification the process of removing the effects of image perspective (tilt) and relief (terrain) effects for the purpose of creating a planimetrically correct image.

• The resultant orthorectified image has a constant scale wherein features are represented in their 'true' positions.

https://trac.osgeo.org/ossim/wiki/orthorectification


Comparison: raw image vs orthoimage

• On the right are 2 images of the same geographic area.

• Note the road that passes through the mountains.

• In the left "raw" or unrectified image, the road appears to be crooked when in fact it is not.

• The image on the right has been orthorectified image and the road appears planimetrically correct.

Source: https://trac.osgeo.org/ossim/wiki/orthorectification


Source: https://online.wr.usgs.gov/ngpo/doq/images/doq2_570.jpg


Orthorectification Requirements

• The camera model and the internal (or in other term: interior) orientation data

• The external orientation data, and

• A terrain or elevation model, covering the area of the photograph.


Using a DEM in Orthorectification

• Orthorectification uses elevation data (DEM, e.g., DSM) to correct distortion in aerial or satellite imagery.

– Removing the influence of topography on imagery

is one of the most important steps of orthorectification.

– The effect of topography is to tilt features in an image away from the center of the camera, and this is counter-acted by employing an elevation model in the orthorectification process. Using a DSM, the tilted features will be untilted!


Orthorectification Procedures

• The algorithm computes the real spatial position of all image pixels.

• Then the image is resampled, using these positions, into a target coordinate system, which was pre-selected by the user.

• To accomplish this step, we shall know also the elevation of the image points; that‘s why a terrain or elevation model is asked for by the algorithm.

http://elte.prompt.hu/sites/default/files/tananyagok/MapGridsAndDatums/ch09s05.html


Orthorectification Procedures

• The result should be always verified, e.g. by a topographic map (practically the one used for control point definition).

• The horizontal fit is usually the best near to the corner that is the closest one to the vertical axis from the camera.

• The fit could be unacceptably poor around the far corner, which is caused by the errors of the external element estimation.

http://elte.prompt.hu/sites/default/files/tananyagok/MapGridsAndDatums/ch09s05.html


Resampling

• Normally, the aerial photo/image pixel matrix will not be parallel to the orthoimage pixel matrix – The pixel sizes of both

images will also differ

• Resampling is needed to determine the new DN value for the new (ortho) image

• Resampling methods: – Nearest neighbor – Bilinear – Cubic convolution



Resampling Methods

• Nearest neighbor – The DN of the

transformed (―orthorectified‖ pixel is equal to the DN of its closest (―original‖) pixel

– Advantages: • simple to implement • Avoids the alteration of

the original pixel values

– Disadvantage: • Features in the output

image may be offset spatially by up to one-half pixel causes disjointed (“blocky”) appearance in the output image


Resampling Methods

• Bilinear interpolation – The DN of the transformed (―orthorectified‖) pixel is

equal to distance-weighted average of the 4 nearest (original) pixels

– Advantage: • Smoother image appearance

– Disadvantage: • Alters the original DN values

Nearest neighbor Bilinear interpolation


Resampling Methods

• Bicubic interpolation or cubic convolution – The DN of the transformed

(―orthorectified‖) pixel is determined by evaluating the block of 16 pixels in the original image that surrounds the output pixel

– Advantage: • Smoother image appearance

• Slightly sharper image than the bilinear interpolation method

– Disadvantage: • Alters the original DN values


Important

• The geometric accuracy of an orthoimage is highly dependent on the accuracy of the DSM used

• Ideally, the spatial resolution of the DSM must be the same as the spatial resolution of the image


Demo: Orthoimage Generation Using LISA FOTO


Online Resource

• A good lecture about orthophoto/orthoimage generation:

– https://www.youtube.com/watch?v=-G1czNrTGCw

https://www.youtube.com/watch?v=-G1czNrTGCw




Reference/Reading Assignment

• Linder, W., 2016. Digital Photogrammetry – A Practical Course, Springer-Verlag Berlin Heidelberg, Germany, pages 38-59.

(See Facebook page for the PDF link)

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