An Ensemble-Based Stacked Sequential Learning Algorithm for Remote Sensing Imagery Classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPereira, Danillo R.-
Autor(es): dc.creatorPisani, Rodrigo J.-
Autor(es): dc.creatorSouza, Andre N. de-
Autor(es): dc.creatorPapa, Joao P.-
Data de aceite: dc.date.accessioned2021-03-10T23:53:57Z-
Data de disponibilização: dc.date.available2021-03-10T23:53:57Z-
Data de envio: dc.date.issued2018-11-26-
Data de envio: dc.date.issued2018-11-26-
Data de envio: dc.date.issued2017-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/JSTARS.2016.2645820-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/162682-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/162682-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionProcesso FAPESP: 2013/20387-7-
Descrição: dc.descriptionProcesso FAPESP: 2014/16250-9-
Descrição: dc.descriptionProcesso FAPESP: 2014/12236-1-
Descrição: dc.descriptionCNPq: 303182/2011-3-
Descrição: dc.descriptionCNPq: 470571/2013-6-
Descrição: dc.descriptionCNPq: 487032/2012-8-
Descrição: dc.descriptionCNPq: 306166/2014-3-
Descrição: dc.descriptionContextual-based image classification attempts at considering spatial/temporal information during the learning process in order to make the classification process smarter. Sequential learning techniques are one of the most used ones to perform contextual classification, being based on a two-step classification process, in which the traditional noncontextual learning process is followed by one more step of classification based on an extended feature vector. In this paper, we propose two ensemble-based approaches to make sequential learning techniques less prone to errors, since their effectiveness is strongly dependent on the feature extension process, which ends up adding the wrong predicted label of the neighborhood samples as new features. The proposed approaches are validated in the context of land-cover classification, being their results considerably better than some state-of-the-art techniques in the literature.-
Formato: dc.format1525-1541-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing-
Relação: dc.relation1,547-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectLand-cover classification-
Palavras-chave: dc.subjectoptimum-path forest (OPF)-
Palavras-chave: dc.subjectsequential learning-
Título: dc.titleAn Ensemble-Based Stacked Sequential Learning Algorithm for Remote Sensing Imagery Classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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