›› 2014, Vol. 34 ›› Issue (4): 46-.doi: 10.3780/j.issn.1000.758X.2014.04.007

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SpectralAnalysisforSubpixelMaterialsBasedonKernelPartialNonnegativeMatrixFactorization

 CUI  Jian-Tao, LI  Xiao-Run, ZHAO  Liao-Ying   

  1. (1CollegeofElectricalEngineering,ZhejiangUniversity,Hangzhou310027)(2InstituteofComputerApplicationTechnology,HangzhouDianziUniversity,Hangzhou310018)
  • Online:2014-08-25 Published:2014-08-25

Abstract: Toimprovetheaccuracyofspectralanalysisforsubpixelmaterialsfurther,anonlinearunmixingalgorithmbasedonthekernelpartialnonnegativematrixfactorization(KPNMF)wasproposed.Firstly,anendmemberextractionalgorithmbasedonthetheoryofconvexgeometrywasusedtogenerateacandidatepixelsetofpureendmembers,andthenthepureendmemberwasdeterminedaccordingtothespatialpurityindicesofthecandidatepixels.Giveninformationofpureendmembers,thekernelmethodwasadoptedtoextendpartialnonnegativematrixfactorization(PNMF).Thecorrespondingobjectivefunctionwasconstructed,andtheiterativesolutionwasalsoderivedtoobtainthesubpixelendmembersandabundancesofalltheendmembers.Theexperimentalresultsdemonstratethattheproposedunmixingalgorithmhasgoodnonlinearunmixingability,andtheunmixingresultsarebetterthanthoseoflinearunmixingalgorithms.

Key words: Hyperspectralunmixing, Subpixel, Convexgeometry, Spatialpurityindex, Partialnonnegativematrixfactorization, Spaceremotesensing