关键词: 高光谱解混, 亚像元, 凸面几何, 空间纯度指数, 部分非负矩阵分解, 航天遥感

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