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Fisher分布下基于白化滤波的极化SAR图像CFAR检测方法.docx

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Fisher分布下基于白化滤波的极化SAR图像CFAR检测方法 Title:ACFARDetectionMethodforPolarimetricSARImageBasedonWhiteningFilterunderFisherDistribution Introduction: PolarimetricSyntheticApertureRadar(SAR)techniquehasgainedsignificantattentionduetoitscapabilitytoobtainvaluableinformationaboutthetargetscatteringpropertiesanditsabilitytodiscriminatebetweendifferenttypesoftargets.However,thedetectionoftargetsinpolarimetricSARimagesremainsachallengingtaskduetothepresenceofspecklenoiseandclutter.ConstantFalseAlarmRate(CFAR)detectionmethodiswidelyusedtoenhancethedetectionperformanceinSARimagery.Inthispaper,weproposeaCFARdetectionmethodbasedonwhiteningfilterunderFisherdistributiontoimprovethedetectionperformanceofpolarimetricSARimages. Methodology: 1.DataPreprocessing: a.Multilookspecklereduction:Inordertomitigatetheeffectofspecklenoise,weapplymultilookprocessingtoreducethespecklelevel. b.Whiteningfilter:ThewhiteningfilterisappliedtodecorrelatethepolarimetricSARdataandenhancethetargetsignature. 2.FisherDistribution: a.TheFisherdistributionmodelisemployedtodescribethestatisticaldistributionofthepolarimetricdataafterwhiteningfilterprocessing. b.FisherdistributionprovidesagoodfitforpolarimetricSARdatacomparedtootherdistributions. 3.CFARDetection: a.Localsub-windowprocessing:ThepolarimetricSARimageisdividedintosmallsub-windowsforCFARprocessingtodetectthepresenceoftargets. b.CFARthresholdestimation:TheestimatedthresholdiscalculatedbasedonthewhiteningfilteroutputandtheFisherdistributionmodel,ensuringaconstantfalsealarmrate. c.Targetdetection:TheCFARthresholdiscomparedwiththelocalintensitytodeterminewhetheratargetispresentornot. 4.Post-processing: a.Falsealarmsuppression:Post-processingtechniques,suchasmorphologicaloperationsandconnectedcomponentanalysis,canbeappliedtosuppressfalsealarmsandenhancethedetectionperformance. b.Targetclassification:Oncethetargetsaredetected,variousclassificationalgorithmscanbeemployedtoclassifythedetectedtargetsbasedontheirscatteringcharacteristics. ResultsandDiscussion: Thep