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dc.contributor.authorPereira, D.
dc.contributor.authorPisani, R.
dc.contributor.authorNakamura, R.
dc.contributor.authorPapa, J. [UNESP]
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), v. 2015-November, p. 76-79.
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.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)
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
dc.contributor.institutionUniversity of Western São Paulo
dc.contributor.institutionBig Data Brasil
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUniversity of Western São Paulo Department of Computing
dc.description.affiliationBig Data Brasil
dc.description.affiliationSão Paulo State University Department of Computing
dc.description.affiliationUnespSão Paulo State University Department of Computing
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