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双约束深度卷积网络的高光谱图像空谱解混方法
Title:ADual-ConstraintDeepConvolutionalNetworkforSpectralUnmixingofHyperspectralImages
Abstract:
Hyperspectralimagery(HSI)providesdetailedspectralinformationforeachpixelinanimage,enablingawiderangeofapplicationssuchastargetdetection,landcoverclassification,andenvironmentalmonitoring.However,duetothepresenceofspectralmixingeffects,theobservedHSIpixelsoftencontaincontributionsfrommultipleunderlyingmaterials,makingitchallengingtoaccuratelyanalyzeandinterpretthespectralsignatures.Spectralunmixingaimstoestimatetheabundancefractionsofeachmaterial,allowingforaquantitativeanalysisofthescene.
Inthispaper,weproposeanovelapproachforspectralunmixingofhyperspectralimagesusingadual-constraintdeepconvolutionalnetwork.Theproposedmethodintegratestheadvantagesofdeeplearningtechniquesandspectralunmixingalgorithmstoimprovetheaccuracyandefficiencyofspectralunmixing.
1.Introduction
Spectralunmixinghasbeenawell-studiedproblemintheremotesensingcommunity.Traditionalmethodsinvolveusinglinearornonlinearmodelstoestimatetheendmemberspectraandabundancefractions.However,thesemethodsoftensufferfromlimitationslikeassumingpureendmembersandGaussiannoise,whichmaynotholdinreal-worldscenarios.Deeplearningtechniques,ontheotherhand,haveshownremarkablesuccessinvariouscomputervisiontasks,includingimagesegmentationandrecognition.Webelievethatemployingdeeplearningapproachescanbenefitspectralunmixingaswell.
2.Dual-ConstraintDeepConvolutionalNetwork
Theproposedmethodutilizesadual-constraintdeepconvolutionalnetwork(DCDCN)thattakesadvantageofbothspatialandspectralinformationinhyperspectralimages.TheDCDCNconsistsofmultipleconvolutionalandpoolinglayersfollowedbyfullyconnectedlayers.Thenetworkistrainedusinglabeleddatawithknownendmemberspectraandtheircorrespondingabundancemaps.Thenetworklearnstoextractdiscriminativefeaturesfromthespectralandspatialdimensionsoftheinputdata,enablingaccuratespectralunmixing.
3.TrainingProcedure
WedescribethetrainingprocedurefortheDCDCN,includingthedatapreparation,n