dc.contributor.author | Pereira, D. | |
dc.contributor.author | Pisani, R. | |
dc.contributor.author | Nakamura, R. | |
dc.contributor.author | Papa, J. [UNESP] | |
dc.date.accessioned | 2022-04-28T19:03:28Z | |
dc.date.available | 2022-04-28T19:03:28Z | |
dc.date.issued | 2015-11-10 | |
dc.identifier | http://dx.doi.org/10.1109/IGARSS.2015.7325701 | |
dc.identifier.citation | International Geoscience and Remote Sensing Symposium (IGARSS), v. 2015-November, p. 76-79. | |
dc.identifier.uri | http://hdl.handle.net/11449/220599 | |
dc.description.abstract | Sequential 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.extent | 76-79 | |
dc.language.iso | eng | |
dc.relation.ispartof | International Geoscience and Remote Sensing Symposium (IGARSS) | |
dc.source | Scopus | |
dc.subject | Land-cover classification | |
dc.subject | Optimum-Path Forest | |
dc.subject | Sequential Learning | |
dc.title | Land-cover classification through sequential learning-based optimum-path forest | en |
dc.type | Trabalho apresentado em evento | |
dc.contributor.institution | University of Western São Paulo | |
dc.contributor.institution | Big Data Brasil | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.description.affiliation | University of Western São Paulo Department of Computing | |
dc.description.affiliation | Big Data Brasil | |
dc.description.affiliation | São Paulo State University Department of Computing | |
dc.description.affiliationUnesp | São Paulo State University Department of Computing | |
dc.identifier.doi | 10.1109/IGARSS.2015.7325701 | |
dc.identifier.scopus | 2-s2.0-84962624104 | |