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基于超像素的高分辨率遥感影像功能区分类.docx

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基于超像素的高分辨率遥感影像功能区分类 Title:FunctionalityZoneClassificationofHigh-ResolutionRemoteSensingImagesBasedonSuperpixels Abstract: High-resolutionremotesensingimagesprovidedetailedandvaluableinformationaboutdifferentlandcovertypes.Accurateclassificationoftheseimagesintofunctionalityzonescanaidinvariousapplicationssuchasurbanplanning,agriculture,andenvironmentalmonitoring.Thispaperintroducesamethodbasedonsuperpixelstoachievehigh-resolutionremotesensingimageclassificationbysegmentingtheimageintocompactandperceptuallyhomogeneousregions.Theproposedapproachhasbeenevaluatedagainstapubliclyavailabledatasetandhasdemonstratedpromisingresultsintermsofaccuracyandefficiency. 1.Introduction: High-resolutionremotesensingimagescontainvastamountsofdata,makingmanualinterpretationandanalysistime-consumingandchallenging.Toovercomethis,automatedimageclassificationtechniqueshavebeenwidelyadopted.However,traditionalpixel-basedclassificationmethodsresultinlossofspatialcontextandoftenstruggletoaccuratelyclassifycomplexscenes.Superpixel-basedsegmentationprovidesasuitablesolutionbygroupingpixelsintocompactregions,preservingthespatialinformationandimprovingclassificationaccuracy.Thispaperpresentsanovelapproachusingsuperpixelsforfunctionalityzoneclassificationinhigh-resolutionremotesensingimages. 2.Methodology: 2.1Preprocessing: Theinitialstepinvolvespreprocessingtheremotesensingimagetoenhanceitsqualityandremovenoise.Commonpreprocessingtechniquessuchasradiometriccorrection,atmosphericcorrection,andnoisereductionareappliedtoimprovetheoverallimagequalityandfacilitatesubsequentanalysis. 2.2SuperpixelSegmentation: Superpixelsegmentationaimstopartitionanimageintocompactandperceptuallyhomogeneousregions.Inthispaper,theSLIC(SimpleLinearIterativeClustering)algorithmisemployedforsuperpixelsegmentationduetoitsefficiencyandeffectivenessinpreservingboundaries.Thenumberofsuperpixelscanbeadjustedbasedonthecomplexityoftheimageandthedesiredlevelofdetail. 2.3FeatureExtraction: Afterobtainingsuperpixels,asetoffeaturesis