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1、ISBN1-8462-xxxx-xProceedingofthe8thinternationalsymposiumonspatialaccuracyassessmentinnaturalresourcesandenvironmentalsciencesShanghai,June25-27,2008,pp.xxx-xxx(Willbesetbythepublisher)Amethodtoincorporateuncertaintyintheclassificationofremotesensingimages(1)(2,3)(4)(5,6)LuísaMS
2、Gonçalves,CidáliaFonte,EduardoNBSJúlio,MarioCaetano1PolytechnicInstituteofLeiria,SchoolofTechnologyandManagement,Portugal2InstituteforSystemsandComputersEngineeringatCoimbra,Portugal3DepartmentofMathematics,UniversityofCoimbra,P–3001454Coimbra,Portugal4ISISE,CivilEngineeringDepa
3、rtment,UniversityofCoimbra5PortugueseGeographicInstitute(IGP),RemoteSensingUnit(RSU),Lisboa,Portugal6CEGI,InstitutoSuperiordeEstatísticaeGestãodeInformação,ISEGI,UniversidadeNovadeLisboa,1070-312Lisboa,PortugalAbstract.Theauthorsanalyzeinthispaperwhethertheintroductionoftheuncer
4、taintyassociatedtotheclassificationofsurfaceelementsintheclassificationoflandscapeunitscanimprovetheresultsaccuracy.Tothisend,ahybridclassificationmethodisdeveloped,incorporatinguncertaintyinformationintheautomaticclassificationofveryhighspatialresolutionmultispectralsatelliteim
5、agestoobtainamapoflandscapeunits.Thedevelopedclassificationmethodologyincludesthefollowingsteps:1)asoftpixel-basedclassification;2)computationoftheclassificationuncertainty;3)imagesegmentation;and4)objectclassificationbasedondecisionrules.Thefirststepoftheproposedmethodologyisth
6、esoftpixel-basedclassificationperformedwiththemaximumlikelihoodclassifier,aimingtoidentifythesurfaceelements(e.g.,treecrown,shade,baresoil,buildings).Subsequentlytheposteriorprobabilitiesarecomputedtoallpixelsoftheimage.Thisinformationenablesthecomputationoftheclassificationunce
7、rtainty.Animagesegmentationisthenmadetoobtainimage-objects.Theclassificationoftheresultingobjectsintolandscapeunitsisperformedconsideringasetofdecisionrulesthatincorporatetheprobabilitiesassignedtotheseveralclassesateachpixelandthedegreeofuncertaintyassociatedtotheseassignments.
8、Theproposedmethodologywastestedontheclassificat
