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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorLI, G.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.contributor.authorHETRICK, S.pt_BR
dc.contributor.authorFREITAS, C. DA C.pt_BR
dc.contributor.authorSANT'ANNA, S. J.pt_BR
dc.date.accessioned2012-11-22T11:11:11Zpt_BR
dc.date.available2012-11-22T11:11:11Zpt_BR
dc.date.created2012-11-22pt_BR
dc.date.issued2012pt_BR
dc.identifier.citationPesquisa Agropecuária Brasileira, Brasilia, DF, v. 47, n. 9, p. 1185-1208, set. 2012.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/940299pt_BR
dc.descriptionLand use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectData fusionpt_BR
dc.subjectMultiple sensor datapt_BR
dc.subjectNonparametric classifierspt_BR
dc.titleLand use/cover classification in the Brazilian Amazon using satellite images.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-10-28T11:11:11Zpt_BR
dc.subject.nalthesaurusTexturept_BR
dc.format.extent2p. 1185-1208.pt_BR
riaa.ainfo.id940299pt_BR
riaa.ainfo.lastupdate2014-10-28pt_BR
dc.identifier.doidx.doi.org/10.1590/S0100-204X2012000900004pt_BR
dc.contributor.institutionDENGSHENG LU, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; SCOTT HETRICK, INDIANA UNIVERSITY; CORINA DA COSTA FREITAS, INPE; SIDNEI JOÃO SIQUEIRA SANT'ANNA, INPE.pt_BR
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