Abstract: Inordertofurtherimprovetheaccuracyofchangedetectionofremotesensingimagesbasedonindependentcomponentanalysis(ICA)andtosolvetheuncertaintyproblemofsequenceofimagecomponentsseparatedbyICA,achangedetectionmethodbasedonwavelettransformandkernelindependentcomponentanalysis(KICA)wasproposed.Firstly,theremotesensingimagesweredecomposedbywavelettransform,andpartitionedvectorscomposedofhigh-frequencycomponentsandlow-frequencycomponentswereobtained.Thenthepartitionedvectorsweremappedintoahigh-dimensionalfeaturespacebythekernelfunction,andthemutuallyindependentvectorswereseparatedbyICAinthisspace.Finally,accordingtothedifferencesbetweenthehigh-frequencycomponentsoftheseparatedvectors,thechangecomponentwasdistinguishedautomatically.Experimentalresultsoftheproposedmethodandotherthreechangedetectionmethodsproposedrecentlybasedontheprincipalcomponentanalysis(PCA),ICA,KICAweregiven.Andsomeanalysisandquantitativecomparisonsweredone.Alargenumberofexperimentalresultsshowthattheproposedmethodcanseparatechangeinformationofremotesensingimageswithhigheraccuracy,andtheintelligentchangedetectionisrealized.

Key words: Remotesensingimage, Changedetection, Kernelindependentcomponentanalysis, Wavelettransform