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