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1、TheInternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInformationSciences,Vol.38,PartIIASSESSMENTOFEXTENSIONALUNCERTAINTYMODELEDBYRANDOMSETSONSEGMENTEDOBJECTSFROMREMOTESENSINGIMAGESa,ba,c,abbX.Zhao,X.Chen*,L.Tian,T.Wang,A.SteinaStateKeyLabora
2、toryofInformationEngineeringinSurveying,MappingandRemoteSensing,WuhanUniversity,LuoYuroad129,China–(cxl,tianliqiao)@lmars.whu.edu.cnbInternationalInstituteforGeo-InformationScienceandEarthObservation,Hengelosestraat99,Enschede,Netherlands–(xzhao,tiejun,st
3、ein)@itc.nlcTheKeyLabofPoyangLakeEcologicalEnvironmentandResourceDevelopment,JiangxiNormalUniversity,Nanchang,Jiangxi,ChinaCommissionII,WGII/4KEYWORDS:accuracyassessment,uncertainty,randomset,imagesegmentation,PoyangLakeABSTRACT:Anewlydevelopedrandomsetmo
4、delhasbeenappliedtomodeltheextensionaluncertaintyofawetlandpatch.Theobjectiveofthisresearchistoexplorethecorrespondingvariablescollectedonthegroundforvalidatingtheuncertainimageobjectsandtoreportthequalityoftherandomsetmodeling.Theindependentsamplest-test
5、andacorrelationanalysishavebeenusedtoidentifythemainvariables,whereastheoverallaccuracyandKappacoefficientsquantifythequalityoftherandomsetmodel.Theresultsshowthatsignificantcorrelationsexistamongcoveringfunction,CarexcoverageandNDVI.Thissuggeststhattheco
6、veringfunctionoftherandomsetcanbequantifiedandinterpretedadequatelybyNDVIderivedfromsatelliteimagesandCarexcoveragemeasuredinthefield.Inaddition,thecoresetoftherandomsethasanoverallaccuracyof85percentandaKappavalueequal0.54,beinghigherthanthemediansetands
7、upportset.Weconcludethattherandomsetsmodelingofuncertaintyallowsustoperformanadequateaccuracyanalysis.1.INTRODUCTIONobjectsinthefield.Thisisbecauseevenontheground,duetovagueandunambiguousboundaries,thedelineationofConventionalpixel-basedandobject-basedcla
8、ssificationuncertainobjectslikeacitymaybeimpossible.Inaddition,approachesgeneratemapswithexclusivecategories.Thesedetailedreferencedatawhichiscriticalforvalidatingsofthardclassificationsaredesignedformappingdiscrete
