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1、sensorsArticleAnUnsupervisedDeepHyperspectralAnomalyDetectorNingMa1,YuPeng1,ShaojunWang1,*andPhilipH.W.Leong2ID1DepartmentofAutomaticTestandControl,SchoolofElectricalEngineeringandAutomation,HarbinInstituteofTechnology,Harbin150080,China;maning@hit.edu.cn(N.M.);pengyu@hit.edu.cn(Y.P.)2SchoolofE
2、lectricalandInformationEngineering,TheUniversityofSydney,Sydney2006,Australia;philip.leong@sydney.edu.au*Correspondence:wangsj@hit.edu.cn;Tel.:+86-451-86413532(ext.439)Received:29November2017;Accepted:22February2018;Published:26February2018Abstract:Hyperspectralimage(HSI)baseddetectionhasattrac
3、tedconsiderableattentionrecentlyinagriculture,environmentalprotectionandmilitaryapplicationsasdifferentwavelengthsoflightcanbeadvantageouslyusedtodiscriminatedifferenttypesofobjects.Unfortunately,estimatingthebackgrounddistributionandthedetectionofinterestinglocalobjectsisnotstraightforward,and
4、anomalydetectorsmaygivefalsealarms.Inthispaper,aDeepBeliefNetwork(DBN)basedanomalydetectorisproposed.Thehigh-levelfeaturesandreconstructionerrorsarelearnedthroughthenetworkinamannerwhichisnotaffectedbypreviousbackgrounddistributionassumption.Toreducecontaminationbylocalanomalies,adaptiveweights
5、areconstructedfromreconstructionerrorsandstatisticalinformation.ByusingthecodeimagewhichisgeneratedduringtheinferenceofDBNandmodifiedbyadaptivelyupdatedweights,alocalEuclideandistancebetweenundertestpixelsandtheirneighboringpixelsisusedtodeterminetheanomalytargets.Experimentalresultsonsynthetica
6、ndrecordedHSIdatasetsshowtheperformanceofproposedmethodoutperformstheclassicglobalReed-Xiaolidetector(RXD),localRXdetector(LRXD)andthe-state-of-the-artCollaborativeRepresentationdetector(CRD).Keywords:hyperspectralimage;deeplearning;anomalydetection1.IntroductionAnHSIanomalytargetisgenerallydefi
7、nedasapixelorobjectwhichhasloweroccurrenceprobabilitythanthebackground[1].Examplesincludepixelsfromaburningtreeinaforestoroilspillsinthesea.Utilizinghundredsofverynarrowandcontinuousspectralbandsandspatialinformation,HSIcanbetterd
