a new road extraction approach for low-frequency sar images based on road appurtenance detection

A NEW ROAD EXTRACTION A NEW ROAD EXTRACTION APPROACH APPROACH FOR LOW-FREQUENCY SAR FOR LOW-FREQUENCY SAR IMAGES BASED ON ROAD APPURTENANCE IMAGES BASED ON ROAD APPURTENANCE DETECTIONDETECTION

Qian Song, Yu-min Wang, Yun-fei Shi, Tian Jin

National University Of Defense TechnologyChangsha, [email protected]

Road ExtractionRoad Extraction

Feature Fusion to Improve Road Network ExtractionFeature Fusion to Improve Road Network Extractionin High-Resolution SAR Imagesin High-Resolution SAR Images

Gianni Lisini and Paolo Gamba of University of Pavia, etc.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 2, APRIL 2006

from

A New Road Extraction Approach for Low-frequency SAR Images Based on Road Appurtenance detectionA New Road Extraction Approach for Low-frequency SAR Images Based on Road Appurtenance detection ，， Qian Song, etc.

Object and CategoryObject and Category

• Conventional Road Extraction Approach is Conventional Road Extraction Approach is targeted attargeted at• Optical ImagesOptical Images• Hyperspectral ImagesHyperspectral Images• High-frequency SAR ImagesHigh-frequency SAR Images

• … … and can be categorized intoand can be categorized into• Line detectionLine detection• Shape inferenceShape inference


Line Detection ApproachLine Detection Approach

• Roads can be approxi-Roads can be approxi-mated as linesmated as lines ，， and can and can be extracted by line/ridge be extracted by line/ridge detection algorithms.detection algorithms.


Shape Inference ApproachShape Inference Approach

• Roads occupy regular shapesRoads occupy regular shapes ，， and can be extracted and can be extracted by edge detection and shape recognition algorithms.by edge detection and shape recognition algorithms.


An Essential PreconditionAn Essential Precondition

• The characteristics of roads and background are The characteristics of roads and background are distinguishabledistinguishable enough. enough.


The Low-frequency SAR CaseThe Low-frequency SAR Case


Data from a flight experiment of an airborne UWB SAR system develop-ed by NUDT, in Summer 2010



The histograms of roads and background (with strong clutters suppressed)

The Low-frequency SAR CaseThe Low-frequency SAR Caseclutterclutter

attenuationattenuation


Alternative WayAlternative Way

Road AppurtenancesRoad Appurtenances


Features Of AppurtenancesFeatures Of Appurtenances• Consistent Scattering CharacteristicsConsistent Scattering Characteristics

• of the same materialof the same material• of the same structureof the same structure• often in the same orientationoften in the same orientation

• Regular Geometrical LayoutsRegular Geometrical Layouts• at nearly fixed intervalsat nearly fixed intervals• with predictable patternswith predictable patterns


Algorithm Algorithm FlowFlow


The Original ImageThe Original Image


ROI ExtractionROI Extraction


After Enhanced Lee FilteringAfter Enhanced Lee Filtering


After Morphologic FilteringAfter Morphologic Filtering


After ClusteringAfter Clustering


ROIs ROIs oof targets and cluttersf targets and clutters

• Features Chosen:Features Chosen:• AreaArea• compactnesscompactness• EccentricityEccentricity• Second order momentSecond order moment• ConvexityConvexity


ROI Discrimi-ROI Discrimi-nationnation


HS-SVMHS-SVM

• An effective One-class classifierAn effective One-class classifier

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i

i

ix

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T. Jin, Z. Zhou, “Ultrawideband synthetic aperture radar landmine detection”, IEEE Trans. on GRS, pp. 3561- 3573, 2007.

After ClassificationAfter Classification


ROI Discrimi-ROI Discrimi-nationnation


Geometrical FeaturesGeometrical Features

• ParallelismParallelism• Constant width and IntervalsConstant width and Intervals• The heading angle changing slowly and continuouslyThe heading angle changing slowly and continuously• Predictable Junction patternsPredictable Junction patterns


After Geometrical DiscriminationAfter Geometrical Discrimination


RoadRoadExtractionExtraction


Final ResultFinal Result


Conclusions and Future worksConclusions and Future works


• ConclusionConclusion• Our approach of road extraction based on appurtenance Our approach of road extraction based on appurtenance

detection shows its effectiveness, when:detection shows its effectiveness, when:• the roads are foliage coveredthe roads are foliage covered• there are some types of appurtenancesthere are some types of appurtenances• Low-frequency SAR is adopted for its penetrating capabilityLow-frequency SAR is adopted for its penetrating capability

• Reasonable selection and exploitation of Reasonable selection and exploitation of sscattering and cattering and geometrical features are key to this approachgeometrical features are key to this approach

• Future worksFuture works• A integration frameworks of conventional and A integration frameworks of conventional and

appurtenance-detection based approachappurtenance-detection based approach• More efficient algorithmsMore efficient algorithms

a new road extraction approach for low-frequency sar images based on road appurtenance detection

Documents

lowfrequency sar images

road extractionfeature

edge detection

lineridge detection

line detection approachroads

airborne uwb sar system

shape recognition algorithms

shape inference approachroads