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dc.contributor.authorunicampFalcão, Alexandre Xavierpt_BR
dc.titleToward satellite-based land cover classification through optimum-path forestpt_BR
dc.contributor.authorPisani, Rodrigo Josépt_BR
dc.contributor.authorNakamura, Rodrigo Yuji Mizobept_BR
dc.contributor.authorRiedel, Paulina Settipt_BR
dc.contributor.authorZimback, Célia Regina Lopespt_BR
dc.contributor.authorFalcão, Alexandre Xavierpt_BR
dc.contributor.authorPapa, João Paulopt_BR
unicamp.authorFalcao, A.X., Institute of Computing, Unicamp-University of Campinas, 13083-859 Campinas, Brazilpt_BR, R.J., Institute of Geoscience and Exact Sciences, Unesp-Universidade Estadual Paulista, 13506-900 Rio-Claro, Brazilpt, R.Y.M., Department of Computer Science, Unesp-Universidade Estadual Paulista, 17040 Bauru, Brazilpt, P.S., Institute of Geoscience and Exact Sciences, Unesp-Universidade Estadual Paulista, 13506-900 Rio-Claro, Brazilpt, C.R.L., School of Agronomic Sciences, Unesp-Universidade Estadual Paulista, 18618-970 Botucatu, Brazilpt, J.P., Department of Computer Science, Unesp-Universidade Estadual Paulista, 17040 Bauru, Brazilpt
dc.subjectReconhecimento de padrõespt_BR
dc.subjectFloresta de caminhos ótimospt_BR
dc.subjectSensoriamento remotopt_BR
dc.subjectImagens de sensoriamento remotopt_BR
dc.subject.otherlanguagePattern recognitionpt_BR
dc.subject.otherlanguageOptimum-path forestpt_BR
dc.subject.otherlanguageRemote sensingpt_BR
dc.subject.otherlanguageRemote-sensing imagespt_BR
dc.description.abstractLand cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can find several works in the literature, we propose a novel methodology for land cover classification by means of the optimum-path forest (OPF) framework, which has never been applied to this context up to date. Experiments were conducted in supervised and unsupervised situations against some state-of-the-art pattern recognition techniques, such as support vector machines, Bayesian classifier, k-means, and mean shift. We had shown that supervised OPF can outperform such approaches, being much faster than all. In regard to clustering techniques, all classifiers have achieved similar results. © 1980-2012 IEEE.en
dc.description.abstractLand cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can fpt_BR
dc.relation.ispartofIEEE transactions on geoscience and remote sensingpt_BR
dc.relation.ispartofabbreviationIEEE trans. geosci. remote sens.pt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.identifier.citationIeee Transactions On Geoscience And Remote Sensing. Institute Of Electrical And Electronics Engineers Inc., v. 52, n. 10, p. 6075 - 6085, 2014.pt_BR
dc.description.sponsordocumentnumber2009/16206-1; 2010/11676-7pt_BR
dc.description.sponsordocumentnumber303182/2011-3; 303673/2010-9pt_BR
dc.description.sponsordocumentnumbersem informaçãopt_BR
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dc.description.provenanceMade available in DSpace on 2015-11-26T15:04:55Z (GMT). No. of bitstreams: 2 2-s2.0-84902077626.pdf: 3610408 bytes, checksum: 85f65e26c9aba0540a28f80acce668d1 (MD5) 2-s2.0-84902077626.pdf.txt: 48326 bytes, checksum: 99d89c4fee89331c96a9c7924b82ef33 (MD5) Previous issue date: 2014en
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dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordLand cover classificationpt_BR
dc.subject.keywordOptimum-path forestpt_BR
dc.subject.keywordRemote sensingpt_BR
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