空-谱融合的条件随机场高光谱影像分类方法
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WEILifei１,YU Ming１,ZHONGYanfei２,YUANZiran１,HUANGCan１
１．FacultyofResourcesandEnvironmentalScience,HubeiUniversity,Wuhan４３００６２,China;２．NationalLaboratory forInformationEngineeringinSurveying,MappingandRemoteSensing,WuhanUniversity,Wuhan４３００７９,China
随着传感器技术的完善同时具备高空间分辨率和高光谱分辨率的遥感数据大量产生与单独的高光谱分辨率遥感数据相比这类数据除了具有丰富而连续的光谱波段还具有较高的空间分辨率但同时也存在影像上地物出现高度的细节化以及同物异谱与同谱异物的现象更加明显的局限降低了光谱可分性15因此单纯利用光谱分类无法满足越来越高的空间分辨率
Abstract:Hyperspectralremotesensingimagehasthecharacteristicsofrichspectralinformationand combiningimagewithspectrum,whichhasbeenwidelyappliedintheearthobservation．Mostoftraditional hyperspectralimageclassificationmodelsdon’tmakefullyuseofspatialfeatureinformation,relytoomuch onthespectralimformation,makingtheclassification accuracystillhave alotofroom toimprove． Conditionalrandomfield (CRF)isakindofprobability modethatcanbetterintegratespatialcontext information．Itplaysamoreandmoreimportantroleinhyperspectralimageclassification．However,most CRFmodelshavetheproblem ofexcesssmoothness,whichwillresultinthelossofdetailinformation． Aimingatthisproblem,thispaperproposedahyperspectralimageclassificationmethodbasedonspaceＧ spectralfusionconditionalrandomfield．Theproposed methoddesignssuitablepotentialfunctionsina pairwiseconditionalrandomfield model,fusingthespectralandspatialfeaturestoconsiderthespatial featureinformationandretainthedetailsineachclass．Theexperimentsontwosetsofhyperspectralimage showedthat,compared withthetraditionalmethods,theproposedclassification methodcaneffectively improvetheclassificationaccuracy,protecttheedgesandshapesofthefeatures,andrelieveexcessive smoothing,whileretainingdetailedinformation． Key words:hyperspectralremote sensingimagery;conditionalrandom field;spaceＧspectralfusion; imageclassification Foundationsupport:TheNationalKeyResearchandDevelopmentProgramofChina (No．２０１７YFB０５０４２０２); TheNationalNaturalScienceFoundationofChina (No．４１６２２１０７);TheSpecialProjectsforTechnological InnovationinHubei(No．２０１８ABA０７８);TheOpenFundofKeyLaboratoryofMinistryofEducationforSpatial DataMiningandInformationSharing(No．２０１８LSDMIS０５);TheOpenFundofKeyLaboratoryofAgricultural RemoteSensingoftheMinistryofAgriculture (No．２０１７０００７)
第 ４９ 卷 第 ３ 期
２０ ２０ 年 ３ 月
测 绘 学 报 ActaGeodaeticaetCartographicaSinica
Vol．４９,No．３ March,２０２０
引文格式:魏立飞,余铭,钟燕飞,等．空Ｇ谱融合的条件随机场高光谱影像分 类 方 法[J]．测 绘 学 报,２０２０,４９(３):３４３Ｇ３５４．DOI:１０．１１９４７/j． AGCS．２０２０．２０１９００４２． WEI Lifei,YU Ming,ZHONG Yanfei,etal．Hyperspectralimage classification method based on spaceＧspectralfusion conditionalrandom field[J]．Acta Geodaetica et Cartographica Sinica,２０２０,４９(３):３４３Ｇ３５４．DOI:１０．１１９４７/j．AGCS． ２０２０．２０１９００４２．
空Ｇ谱融合的条件随机场高光谱影像分类方法
魏 立 飞１,余 铭１,钟 燕 飞２,袁 自 然１,黄 灿１
１．湖北大学资源环境学院,湖 北 武 汉 ４３００６２;２．武 汉 大 学 测 绘 遥 感 信 息 工 程 国 家 重 点 实 验 室,湖 北 武汉 ４３００７９
Hale Waihona Puke Hyperspectralimageclassification method basedonspaceＧspectralfusion conditionalrandomfield
１．FacultyofResourcesandEnvironmentalScience,HubeiUniversity,Wuhan４３００６２,China;２．NationalLaboratory forInformationEngineeringinSurveying,MappingandRemoteSensing,WuhanUniversity,Wuhan４３００７９,China
随着传感器技术的完善同时具备高空间分辨率和高光谱分辨率的遥感数据大量产生与单独的高光谱分辨率遥感数据相比这类数据除了具有丰富而连续的光谱波段还具有较高的空间分辨率但同时也存在影像上地物出现高度的细节化以及同物异谱与同谱异物的现象更加明显的局限降低了光谱可分性15因此单纯利用光谱分类无法满足越来越高的空间分辨率
Abstract:Hyperspectralremotesensingimagehasthecharacteristicsofrichspectralinformationand combiningimagewithspectrum,whichhasbeenwidelyappliedintheearthobservation．Mostoftraditional hyperspectralimageclassificationmodelsdon’tmakefullyuseofspatialfeatureinformation,relytoomuch onthespectralimformation,makingtheclassification accuracystillhave alotofroom toimprove． Conditionalrandomfield (CRF)isakindofprobability modethatcanbetterintegratespatialcontext information．Itplaysamoreandmoreimportantroleinhyperspectralimageclassification．However,most CRFmodelshavetheproblem ofexcesssmoothness,whichwillresultinthelossofdetailinformation． Aimingatthisproblem,thispaperproposedahyperspectralimageclassificationmethodbasedonspaceＧ spectralfusionconditionalrandomfield．Theproposed methoddesignssuitablepotentialfunctionsina pairwiseconditionalrandomfield model,fusingthespectralandspatialfeaturestoconsiderthespatial featureinformationandretainthedetailsineachclass．Theexperimentsontwosetsofhyperspectralimage showedthat,compared withthetraditionalmethods,theproposedclassification methodcaneffectively improvetheclassificationaccuracy,protecttheedgesandshapesofthefeatures,andrelieveexcessive smoothing,whileretainingdetailedinformation． Key words:hyperspectralremote sensingimagery;conditionalrandom field;spaceＧspectralfusion; imageclassification Foundationsupport:TheNationalKeyResearchandDevelopmentProgramofChina (No．２０１７YFB０５０４２０２); TheNationalNaturalScienceFoundationofChina (No．４１６２２１０７);TheSpecialProjectsforTechnological InnovationinHubei(No．２０１８ABA０７８);TheOpenFundofKeyLaboratoryofMinistryofEducationforSpatial DataMiningandInformationSharing(No．２０１８LSDMIS０５);TheOpenFundofKeyLaboratoryofAgricultural RemoteSensingoftheMinistryofAgriculture (No．２０１７０００７)
第 ４９ 卷 第 ３ 期
２０ ２０ 年 ３ 月
测 绘 学 报 ActaGeodaeticaetCartographicaSinica
Vol．４９,No．３ March,２０２０
引文格式:魏立飞,余铭,钟燕飞,等．空Ｇ谱融合的条件随机场高光谱影像分 类 方 法[J]．测 绘 学 报,２０２０,４９(３):３４３Ｇ３５４．DOI:１０．１１９４７/j． AGCS．２０２０．２０１９００４２． WEI Lifei,YU Ming,ZHONG Yanfei,etal．Hyperspectralimage classification method based on spaceＧspectralfusion conditionalrandom field[J]．Acta Geodaetica et Cartographica Sinica,２０２０,４９(３):３４３Ｇ３５４．DOI:１０．１１９４７/j．AGCS． ２０２０．２０１９００４２．
空Ｇ谱融合的条件随机场高光谱影像分类方法
魏 立 飞１,余 铭１,钟 燕 飞２,袁 自 然１,黄 灿１
１．湖北大学资源环境学院,湖 北 武 汉 ４３００６２;２．武 汉 大 学 测 绘 遥 感 信 息 工 程 国 家 重 点 实 验 室,湖 北 武汉 ４３００７９
Hale Waihona Puke Hyperspectralimageclassification method basedonspaceＧspectralfusion conditionalrandomfield