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结合分层和ADMM的高光谱图像解混方法.docx

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结合分层和ADMM的高光谱图像解混方法 Title:AHybridHyperspectralImageUnmixingMethodusingLayeredandADMMApproaches Abstract: Hyperspectralimageunmixingisafundamentaltaskinremotesensing,aimingtoestimatetheconstituentsandtheirabundancesinmixedpixelspectra.Thispaperpresentsanovelapproachthatcombinesthelayeredandalternatingdirectionmethodofmultipliers(ADMM)techniquestoimprovetheaccuracyandefficiencyofhyperspectralimageunmixing.Theproposedmethodleveragestheadvantagesofbothapproaches,enablingenhancedunmixingresultsandfasterconvergence. 1.Introduction Hyperspectralimagingsystemscapturedataacrossawiderangeofwavelengths,providingdetailedinformationabouttheEarth'ssurface.However,theacquiredimageryoftenrepresentsmixturesofdifferentmaterialswithineachpixel,makingitcrucialtounmixthespectraandestimatethepurecomponents'abundances.Traditionalunmixingalgorithms,suchaslinearspectralunmixing(LSU),relyonlinearmodelsandoftenassumepixelwiseadditivityandGaussiannoise.Unfortunately,theseassumptionsmaynotholdinpractice,limitingtheaccuracyoftheunmixingresults. 2.LayeredHyperspectralImageUnmixing Layeredunmixinghasemergedasapowerfultechniqueforhandlingspectrallyvaryingmaterialsandnon-linearmixtures.Thisapproachdecomposesthemixedpixelspectraintomultiplelayers,eachrepresentingadistinctpurematerial.Thenumberoflayersisusuallyunknownandneedstobeestimated.Inthismethod,ahierarchicallystructuredBayesianmodelisemployed,whichincorporatespriorknowledgeaboutthematerialsignaturesandtheirspectralvariationsacrossdifferentregionsoftheimage.Thisallowsforadaptiveandspatiallyvaryingdecompositionofmixedpixelsintopurecomponents. 3.ADMMinHyperspectralImageUnmixing Thealternatingdirectionmethodofmultipliersisapopularoptimizationtechniquethatcansolveproblemswithseparableobjectivefunctions.ADMMhasbeensuccessfullyappliedtohyperspectralimageunmixingasitcanhandlenon-negativityconstraintsandsparsity-promotingpenaltyterms.ADMMdividestheunmixingproblemintosmallersubproblems,whichcanbesolvedefficientlyusingiterativeupdates.TheADMMalgorithmalte