Land-cover classification through sequential learning-based optimum-path forest

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dc.contributor.authorPereira, D.
dc.contributor.authorPisani, R.
dc.contributor.authorNakamura, R.
dc.contributor.authorPapa, J. [UNESP]
dc.contributor.institutionUniversity of Western São Paulo
dc.contributor.institutionBig Data Brasil
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T19:03:28Z
dc.date.available2022-04-28T19:03:28Z
dc.date.issued2015-11-10
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.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
dc.format.extent76-79
dc.identifierhttp://dx.doi.org/10.1109/IGARSS.2015.7325701
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), v. 2015-November, p. 76-79.
dc.identifier.doi10.1109/IGARSS.2015.7325701
dc.identifier.scopus2-s2.0-84962624104
dc.identifier.urihttp://hdl.handle.net/11449/220599
dc.language.isoeng
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)
dc.sourceScopus
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

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