Publicação: Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification
dc.contributor.author | Osaku, Daniel | |
dc.contributor.author | Pereira, Danillo R. [UNESP] | |
dc.contributor.author | Levada, Alexandre L. M. | |
dc.contributor.author | Papa, Joao P. [UNESP] | |
dc.contributor.institution | Universidade Federal de São Carlos (UFSCar) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2018-11-27T13:40:00Z | |
dc.date.available | 2018-11-27T13:40:00Z | |
dc.date.issued | 2016-05-01 | |
dc.description.abstract | Contextual-based learning aims at considering neighboring pixels to improve pixelwise-oriented classification techniques. In this letter, we presented a metaheuristic framework for the optimization of nondiscrete Markovian models considering the optimum-path forest (OPF) classifier, and we proposed a post-processing procedure to avoid overcorrection over high-frequency regions. The proposed approach outperformed previous results obtained with standard OPF in satellite imagery. | en |
dc.description.affiliation | Univ Fed Sao Carlos, Dept Comp Sci, BR-13565905 Sao Carlos, SP, Brazil | |
dc.description.affiliation | Sao Paulo State Univ, Dept Comp, BR-01049010 Sao Paulo, Brazil | |
dc.description.affiliationUnesp | Sao Paulo State Univ, Dept Comp, BR-01049010 Sao Paulo, Brazil | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
dc.description.sponsorshipId | FAPESP: 2012/06472-9 | |
dc.description.sponsorshipId | FAPESP: 2013/20387-7 | |
dc.description.sponsorshipId | FAPESP: 2014/16250-9 | |
dc.description.sponsorshipId | CNPq: 303182/2011-3 | |
dc.description.sponsorshipId | CNPq: 470571/2013-6 | |
dc.description.sponsorshipId | CNPq: 306166/2014-3 | |
dc.format.extent | 735-739 | |
dc.identifier | http://dx.doi.org/10.1109/LGRS.2016.2541458 | |
dc.identifier.citation | Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 13, n. 5, p. 735-739, 2016. | |
dc.identifier.doi | 10.1109/LGRS.2016.2541458 | |
dc.identifier.file | WOS000375274700026.pdf | |
dc.identifier.issn | 1545-598X | |
dc.identifier.uri | http://hdl.handle.net/11449/165152 | |
dc.identifier.wos | WOS:000375274700026 | |
dc.language.iso | eng | |
dc.publisher | Ieee-inst Electrical Electronics Engineers Inc | |
dc.relation.ispartof | Ieee Geoscience And Remote Sensing Letters | |
dc.rights.accessRights | Acesso aberto | |
dc.source | Web of Science | |
dc.subject | Contextual classification | |
dc.subject | optimum-path forest (OPF) | |
dc.title | Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification | en |
dc.type | Artigo | |
dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
dcterms.rightsHolder | Ieee-inst Electrical Electronics Engineers Inc | |
dspace.entity.type | Publication | |
unesp.campus | Universidade Estadual Paulista (UNESP), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |
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