Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1022621
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Campo DCValorIdioma
dc.contributor.authorLU, D.eng
dc.contributor.authorMAUSEL, P.eng
dc.contributor.authorBATISTELLA, M.eng
dc.contributor.authorMORAN, E.eng
dc.date.accessioned2019-03-26T00:41:23Z-
dc.date.available2019-03-26T00:41:23Z-
dc.date.created2015-08-25
dc.date.issued2003
dc.identifier.citationIn: ASPRS 2003 ANNUAL CONFERENCE, Anchorage, Alaska/EUA. Proceedings... Bethesda: ASPRS, 2003. 11 p.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1022621-
dc.descriptionNumerous classifiers have been developed and different classifiers have their own characteristics. Controversial results often occurred depending on the landscape complexity of the study area and the data used. Therefore, this paper aims to find a suitable classifier for the tropical land cover classification. Five classifiers ? minimum distance classifier (MDC), maximum likelihood classifier (MLC), fisher linear discriminant (FLD), extraction and classification of homogeneous objects (ECHO), and linear spectral mixture analysis (LSMA) ? were tested using Landsat Thematic Mapper (TM) data in the Amazon basin using the same training sample data sets. Seven land cover classes ? mature forest, advanced succession forest, initial secondary succession forest, pasture, agricultural lands, bare lands, and water ? were classified. Overall classification accuracy and kappa analysis were calculated. The results indicate that LSMA and ECHO classifiers provided better classification accuracies than the MDC, MLC, and FLD in the moist tropical region. The overall accuracy of LSMA approach reaches 86% associated with 0.82 kappa coefficienteng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectExtraction and classification of homogeneouseng
dc.subjectFisher linear discriminanteng
dc.subjectMinimum distance classifiereng
dc.titleComparison of land-cover classification methods in the Brazilian Amazon Basin.eng
dc.typeArtigo em anais e proceedingseng
dc.date.updated2019-03-26T00:41:23Z
dc.format.extent211 p.eng
riaa.ainfo.id1022621eng
riaa.ainfo.lastupdate2019-03-25
dc.contributor.institutionDENGSHENG LU, INDIANA UNIVERSITY; PAUL MAUSEL, INDIANA STATE UNIVERSITY; MATEUS BATISTELLA, CNPM; EMILIO MORAN, INDIANA UNIVERSITY.eng
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