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基于SVM和能量最小化的PolSAR图像分类方法.docx

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基于SVM和能量最小化的PolSAR图像分类方法 Title:SVM-basedPolSARImageClassificationMethodwithEnergyMinimization Abstract: PolSAR(PolarimetricSyntheticApertureRadar)imageclassificationplaysacriticalroleinvariousremotesensingapplications,suchaslandcovermapping,targetdetection,andenvironmentalmonitoring.Inthispaper,anovelclassificationmethodcombiningSupportVectorMachines(SVM)andenergyminimizationisproposedforPolSARimageclassification.TheproposedmethodtakesadvantageofSVM'scapabilitytohandlehigh-dimensionaldataandoffersrobustnessagainstnoiseandsparsesamples.Furthermore,energyminimizationtechniquesareemployedtorefinetheclassificationresultsandenhancetheoverallaccuracy. Keywords:PolSAR,classification,SVM,energyminimization,remotesensing 1.Introduction PolSARimaging,asanactiveremotesensingtechnique,capturesthefullpolarimetricinformationofasceneusingradarsensors.PolSARdataprovidesvaluableinformationaboutthescatteringmechanismsandtargetcharacteristics,enablingamoreaccurateclassificationofdifferentlandcovertypes.TraditionalPolSARimageclassificationmethodsoftenrelyonstatisticalmodelsorclusteringalgorithms,whichmaynoteffectivelyexploitthecomplexscatteringbehaviorspresentinPolSARdata.Therefore,thereisaneedfornovelmethodstoimprovetheaccuracyandreliabilityofPolSARimageclassification. 2.SupportVectorMachines(SVM) SVMisapowerfulmachinelearningalgorithmthathasbeenwidelyutilizedinremotesensingapplications.SVMusesahyperplanetoclassifydatapointsinahigh-dimensionalfeaturespace,aimingtomaximizethemarginbetweenclasses.SVMhastheabilitytohandlehigh-dimensionaldataandisrobustagainstnoiseandsparsesamples,makingitsuitableforPolSARimageclassification.Inthispaper,weemploySVMastheinitialclassifierforPolSARimageclassification. 3.EnergyMinimization Energyminimizationtechniqueshavebeenwidelyusedincomputervisionforimagesegmentationandclassificationtasks.Inourproposedmethod,energyminimizationisemployedtorefinetheinitialclassificationresultsobtainedfromSVM.Energyminimizationaimstooptimizeanobjectivefunctionbyiterativelymi