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基于图小波变换的纹理分类与SAR图像识别.docx

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基于图小波变换的纹理分类与SAR图像识别 Title:TextureClassificationandSARImageRecognitionBasedonGraphWaveletTransform Abstract: Textureclassificationandsyntheticapertureradar(SAR)imagerecognitionplaycrucialrolesinvariousfieldssuchasremotesensing,objectdetection,andenvironmentalmonitoring.Thispaperexplorestheutilizationofgraphwavelettransform(GWT)fortextureclassificationandSARimagerecognitiontasks.GWTisapowerfultoolforanalyzingsignalsongraph-structureddata,whichcaneffectivelycapturebothlocalandglobalinformation.ByemployingGWT,theproposedmethodachievesexcellentperformanceinbothtextureclassificationandSARimagerecognitiontasks. 1.Introduction 1.1Background 1.2Motivation 1.3Overallstructureofthepaper 2.LiteratureReview 2.1TextureClassificationMethods 2.2SARImageRecognitionTechniques 2.3GraphWaveletTransform 3.ProposedMethodology 3.1PreprocessingofTextureImages 3.2GraphConstruction 3.3GraphWaveletTransform 3.4FeatureExtraction 3.5ClassificationModel 4.ExperimentalResultsandAnalysis 4.1DatasetDescription 4.2EvaluationMetrics 4.3TextureClassificationResults 4.4SARImageRecognitionResults 4.5ComparativeAnalysiswithOtherMethods 5.Discussion 5.1LimitationsoftheProposedMethod 5.2FutureDirectionsforImprovement 6.Conclusion 6.1SummaryoftheMethod 6.2Contributions 6.3PotentialApplications 7.References 1.Introduction: Inrecentyears,textureclassificationandSARimagerecognitionhaveattractedsignificantattentionduetotheirwiderangeofapplications.Traditionalmethodsfortextureclassificationheavilyrelyonhandcraftedfeatures,whicharelimitedincapturingcomplexpatternsandrelationships.Likewise,SARimagerecognitionfaceschallengessuchasspecklenoiseandlowspatialresolution.Toovercometheselimitations,theuseofgraphwavelettransform(GWT)hasemergedasapromisingapproach.ThispaperaimstoinvestigatethepotentialofGWTintextureclassificationandSARimagerecognitiontasks. 2.LiteratureReview: Thissectionprovidesanoverviewofexistingmethodsintextureclassification,SARimagerecognition,andgraphwavelettransform.Ithighlightsthelimitationsoftradi