automatic road feature recognition and extraction from remote sensing imagery e.f. granzow iguana...

Automatic Road Feature Recognition and Automatic Road Feature Recognition and Extraction from Remote Sensing ImageryExtraction from Remote Sensing Imagery

E.F. Granzow

Iguana Incorporated

&

David Fletcher

Geographic Paradigm Computing

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Presentation OverviewPresentation Overview

Research ContextBasic ApproachThe IPaver ToolkitAn ExampleFindings and Reflections

Research ContextResearch Context

White paper prepared as resource document for NCRST - Safety, Hazards and Disaster Assessment

Research and software developed as part of NASA supported ARC project “Development and Automation of High Resolution Image Extraction Methodologies for Transportation Features”

Research ContextResearch Context

Problem Statement

Feasibility of automating extraction of transportation features and potential degrees of automation

Development and use of microcomputer based and specially developed software in component environment

Economic rationale for commercial applications in this area

Basic ApproachBasic Approach

Key Concepts Roadway network considered as single object in

feature identification process Image elements regarded in terms of estimated

probability of inclusion in solution set A priori assumption elements are not part of road

network object Approach has roots in pattern recognition and

computer vision

Basic ApproachBasic Approach

Key Concepts (Continued) Based on user directed iterative application of

tools Provides immediate feedback on progress Scaled for interactive usage

Basic ApproachBasic Approach

Roadway Network as Object - Benefits

Flexible Problem/Image Segmentation Processing Efficiencies Global/Reusable Classification/Processing Model

The IPaver ToolkitThe IPaver ToolkitFunctions

Recomputes DNs based on mean and offsets into n equally spaced classes Merges two images with user specified weightings Calculates given statistic for specified kernel and replaces kernel DN with value Deletes image features based on a combination of size and morphology

The IPaver ToolkitThe IPaver ToolkitFunctions (continued)

Identifies and eliminates tenuous connections between features based on pixel strings of varying types

Uses pixel distance map to develop single string representation of linear features Uses width/member seeds to trace and draw road elements

The IPaver ToolkitThe IPaver ToolkitSupport Software

IguanaSpace - Implements custom IPaver interface and parameter management ScionImage - Implements macro procedures to view images and produce DM and

seed files IParse - Tiles images to specified size and overlap Evidence - For known solution reports false and true positives and negatives

IPaver Interface (IguanaSpace)IPaver Interface (IguanaSpace)

Supports both menu and flowchart access to the IPaver toolkit Allows direct editing of each function’s input options and parameters through windows dialogs Automatically logs program states and sequences for review and reuse Will easily accepts changes/additions to IPaver

IPaver Interface (IguanaSpace)IPaver Interface (IguanaSpace)

IPaver … An ExampleIPaver … An Example

1 Meter resolution USGS DOQ

Residential Area

Central Albuquerque

Panchromatic (0-255)

1/2 square km

IPaver … An ExampleIPaver … An Example

Classification by Road Material Type

Parameters

DN Mean - 135

Group Interval - 15

Number of Groups - 4

IPaver … An ExampleIPaver … An Example

Statistical Projection in 3x3 Kernel Neighborhood

Parameters

Statistic - Std Deviation

Kernel Size - 3

IPaver … An ExampleIPaver … An Example

Merging two images

Parameters

Weight I1 - 1.0

Weight I2 - 1.0

IPaver … An ExampleIPaver … An Example

Deletion with Morphological Constraints

Parameters

Max Object Size - 300

Max H/W Object Size - 1500

Min H/W Ratio - .8

IPaver … An ExampleIPaver … An Example

String Filtering

Parameters

Type - cul-de-sac

DN Threshold - 255

Cul-de-sac depth - 2 pixels

IPaver … An ExampleIPaver … An Example

Centerline Development from Distance Map

Parameters

None

IPaver … An ExampleIPaver … An Example

Road Edge Tracing

In development …

Uses seed to identify both “essential line” and road edge

Constrains trace based on degree of curvature and aberrant section length

Controls degree of deviation between EL and road edge path

Uses DM based and “source” image together

IPaver … An ExampleIPaver … An Example

Superimposition of Solution on Base Image

Parameters

None

Findings & ReflectionsFindings & Reflections

The Evidence Model

Model was developed to measure success in delineating image elements both within and outside the travelway

Solution template was developed by hand for 256x256 image thumbnail and compared on pixel by pixel basis to IPaver derived solution

Evaluation phrased as true and false postives and negatives

Findings & ReflectionsFindings & Reflections

The Evidence Model (Continued)

image size is '65536' total road rasters '9517' percent of image '14.5' total true positives '7219' ; pp/tp '75.9' total true negatives '53621' ; nn/tn '95.7' total false positives '2398' ; fp/tn ' 4.3' total false negatives '2298' ; fn/tp '24.1' total efficiency '71.6'

Findings & ReflectionsFindings & Reflections

Project Conclusions - Technical

•It is possible to automate portions of the transportation feature recognition and extraction process

•It’s feasible to do this without use of “legacy” commercial products (i.e. ERDAS Imagine) and large scale hardware

•The probable minimum spatial resolution for IPaver is probably about 1 meter

Findings & ReflectionsFindings & Reflections

Project Conclusions - Economic

•Our original conclusion to not pursue commercial options may be obsolete

•New interest and funding for transportation feature and centerline extraction may present new commercial potentials

•Changes/evolution of image provider licensing policies have enhanced these potentials

•Spaceborne imagery’s near total reliance on defense applications and procurements creates continuing commercial uncertainties

Findings & ReflectionsFindings & Reflections

Some General Observations

•New multi- and hyperspectral high resolution imagery offers avenues to enhance the extraction process

•Urban scenes present greatest challenges due to oblique shadow effects of urban canyons and other urban specific issues

•Likely applications are for suburban/rural high growth and unmapped areas