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dc.contributor.authorPereira, D.
dc.contributor.authorPisani, R.
dc.contributor.authorNakamura, R.
dc.contributor.authorPapa, J. [UNESP]
dc.contributor.authorIEEE
dc.date.accessioned2018-11-26T16:27:55Z
dc.date.available2018-11-26T16:27:55Z
dc.date.issued2015-01-01
dc.identifier.citation2015 Ieee International Geoscience And Remote Sensing Symposium (igarss). New York: Ieee, p. 76-79, 2015.
dc.identifier.issn2153-6996
dc.identifier.urihttp://hdl.handle.net/11449/161290
dc.description.abstractSequential learning-based pattern classification aims at providing more accurate labeled maps by adding an extra step of classification using an augmented feature vector. In this paper, we evaluated the robustness of Optimum-Path Forest (OPF) classifier in the context of land-cover classification using both satellite and radar images, showing OPF can benefit from sequential learning theoretical basis.en
dc.format.extent76-79
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2015 Ieee International Geoscience And Remote Sensing Symposium (igarss)
dc.sourceWeb of Science
dc.subjectLand-cover classification
dc.subjectOptimum-Path Forest
dc.subjectSequential Learning
dc.titleLAND-COVER CLASSIFICATION THROUGH SEQUENTIAL LEARNING-BASED OPTIMUM-PATH FORESTen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
dc.contributor.institutionUniv Western Sao Paulo
dc.contributor.institutionBig Data Brasil
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUniv Western Sao Paulo, Dept Comp, Presidente Prudente, SP, Brazil
dc.description.affiliationBig Data Brasil, Sao Paulo, SP, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.identifier.wosWOS:000371696700020
dc.rights.accessRightsAcesso aberto
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