LAND-COVER CLASSIFICATION THROUGH SEQUENTIAL LEARNING-BASED OPTIMUM-PATH FOREST

dc.contributorUniv Western Sao Paulo
dc.contributorBig Data Brasil
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:27:55Z
dc.date.available2018-11-26T16:27:55Z
dc.date.created2018-11-26T16:27:55Z
dc.date.issued2015-01-01
dc.identifier2015 Ieee International Geoscience And Remote Sensing Symposium (igarss). New York: Ieee, p. 76-79, 2015.
dc.identifier2153-6996
dc.identifierhttp://hdl.handle.net/11449/161290
dc.identifierWOS:000371696700020
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.
dc.languageeng
dc.publisherIeee
dc.relation2015 Ieee International Geoscience And Remote Sensing Symposium (igarss)
dc.rightsAcesso aberto
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 FOREST
dc.typeActas de congresos


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