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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampTorres, Ricardo da Silvapt_BR
dc.contributor.authorunicampSantos, Jefersson Alex dospt_BR
dc.typeArtigopt_BR
dc.titleNature-inspired framework for hyperspectral band selectionpt_BR
dc.contributor.authorNakamura, Rodrigo Y. M.pt_BR
dc.contributor.authorFonseca, Leila Maria Garciapt_BR
dc.contributor.authorSantos, Jefersson Alex dospt_BR
dc.contributor.authorTorres, Ricardo da S.pt_BR
dc.contributor.authorXin-She, Yangpt_BR
dc.contributor.authorPapa, João Papapt_BR
dc.subjectComputação evolutivapt_BR
dc.subjectAlgoritmos heurísticospt_BR
dc.subjectImagem hiperespectralpt_BR
dc.subjectClassificação de imagempt_BR
dc.subjectReconhecimento de padrõespt_BR
dc.subject.otherlanguageEvolutionary computationpt_BR
dc.subject.otherlanguageHeuristic algorithmspt_BR
dc.subject.otherlanguageHyperspectral imagingpt_BR
dc.subject.otherlanguageImage classificationpt_BR
dc.subject.otherlanguagePattern recognitionpt_BR
dc.description.abstractAlthough hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.pt
dc.description.abstractAlthough hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation costpt_BR
dc.relation.ispartofIEEE transactions on geoscience and remote sensingpt_BR
dc.relation.ispartofabbreviationIEEE trans. geosci. remote sens.pt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.date.issued2014pt_BR
dc.date.monthofcirculationApr.pt_BR
dc.identifier.citationIeee Transactions On Geoscience And Remote Sensing. Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 4, n. 2126, n. 2137, 2014.pt_BR
dc.language.isoengpt_BR
dc.description.volume52pt_BR
dc.description.issuenumber4pt_BR
dc.description.firstpage2126pt_BR
dc.description.lastpage2137pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0196-2892pt_BR
dc.identifier.eissn1558-0644pt_BR
dc.identifier.doi10.1109/TGRS.2013.2258351pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/6515634pt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsordocumentnumberFAPESP [2012/18768-0, 2011/14058-5, 2009/16206-1, 2009/18438-7, 2008/58112-0, 2008/58528-2]pt
dc.description.sponsordocumentnumberCNPq [303182/2011-3, 306580/2012-8, 484254/2012-0]pt
dc.description.sponsordocumentnumber2012/18768-0; 2011/14058-5; 2009/16206-1; 2009/18438-7; 2008/58112-0; 2008/58528-2pt_BR
dc.description.sponsordocumentnumber303182/2011-3; 306580/2012-8; 484254/2012-0pt_BR
dc.description.sponsordocumentnumberSEM INFORMAÇÃOpt_BR
dc.date.available2014-07-30T17:04:17Z
dc.date.available2015-11-26T17:40:45Z-
dc.date.accessioned2014-07-30T17:04:17Z
dc.date.accessioned2015-11-26T17:40:45Z-
dc.description.provenanceMade available in DSpace on 2014-07-30T17:04:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2014. Added 1 bitstream(s) on 2021-05-27T19:09:27Z : No. of bitstreams: 1 000329527000018.pdf: 1093791 bytes, checksum: 3dc586d61f275f67187e6256c99cda13 (MD5)en
dc.description.provenanceMade available in DSpace on 2015-11-26T17:40:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2014en
dc.identifier.urihttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/63448
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/63448-
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.departmentSEM INFORMAÇÃOpt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordEvolutionary computationpt_BR
dc.subject.keywordHeuristic algorithmspt_BR
dc.subject.keywordHyperspectral imagingpt_BR
dc.subject.keywordImage classificationpt_BR
dc.subject.keywordPattern recognitionpt_BR
dc.identifier.source000329527000018pt_BR
dc.creator.orcid0000-0001-9772-263Xpt_BR
dc.creator.orcid0000-0002-8889-1586pt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteThe authors gratefully acknowledge the valuable assistance of the revisors in improving their workpt_BR
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