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基于深度学习的高分辨率遥感影像分类研究.docx

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基于深度学习的高分辨率遥感影像分类研究 Abstract Withtherapiddevelopmentofremotesensingtechnology,high-resolutionremotesensingimageshavebecomeafundamentaldatasourceforEarthobservation,urbanplanning,anddisasterprevention.Imageclassificationisoneofthecriticaltasksinremotesensingimageprocessing.Traditionalmethodsforimageclassificationrelyonmanualfeatureextraction,whichistime-consumingandlacksaccuracy.Adeeplearningapproacheffectivelyaddressesthisissuebyautomaticallyextractingfeaturesandhasbeenwidelyusedinimageclassificationtasks.Inthispaper,wefocusontheapplicationofdeeplearningtechniquesforhigh-resolutionremotesensingimageclassificationandprovideanextensivereviewofrelatedworks.WealsoproposeanapproachtoimageclassificationbasedonConvolutionNeuralNetworks(CNN)andevaluateitsperformanceonvariousdatasets.Ourproposedmethodachievedhighaccuracyanddemonstratestheeffectivenessofdeeplearninginhigh-resolutionremotesensingimageclassification. Keywords:RemoteSensing,ImageClassification,DeepLearning,ConvolutionalNeuralNetworks. Introduction Remotesensingtechnologyhasbeenwidelyusedinvariousfieldssuchasurbanplanning,agriculturemonitoring,andenvironmentalprotection.High-resolutionremotesensingimagesprovidecomprehensive,timely,andaccurateinformationforunderstandingtheEarth'ssurface.Imageclassificationisoneofthefundamentaltasksinremotesensingimageprocessing,whichaimstolabelthepixelsinanimagewithdifferentcategories,suchasvegetation,water,urbanland,andsoon.Accurateimageclassificationisessentialforawiderangeofapplications,suchasurbanplanning,landusemonitoring,disasterprevention,andmilitaryintelligence. Traditionalimageclassificationmethodsrelyonmanualfeatureextraction,suchastexture,color,andshape,whichistime-consumingandoftenresultsinlowaccuracyduetothecomplexityandvariabilityofremotesensingimages.Withtherapiddevelopmentofdeeplearningtechniques,automaticfeatureextractionhasbecomefeasible,andmanystudieshavedemonstratedtheeffectivenessofdeeplearningapproachesinimageclassificationtasks. ConvolutionalNeuralNetworks(C