A new approach to contextual learning using interval arithmetic and its applications for land-use classification
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.patrec.2016.03.020 http://hdl.handle.net/11449/162117 |
Resumo: | Contextual-based classification has been paramount in the last years, since spatial and temporal information play an important role during the process of learning the behavior of the data. Sequential learning is also often employed in this context in order to augment the feature vector of a given sample with information about its neighborhood. However, most part of works describe the samples using features obtained through standard arithmetic tools, which may not reflect the data as a whole. In this work, we introduced the Interval Arithmetic to the context of land-use classification in satellite images by describing a given sample and its neighbors using interval of values, thus allowing a better representation of the model. Experiments over four satellite images using two distinct supervised classifiers showed we can considerably improve sequential learning-oriented pattern classification using concepts from Interval Arithmetic. (C) 2016 Elsevier B.V. All rights reserved. |
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Repositório Institucional da UNESP |
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A new approach to contextual learning using interval arithmetic and its applications for land-use classificationSliding WindowSequential learningContextual learningInterval ArithmeticContextual-based classification has been paramount in the last years, since spatial and temporal information play an important role during the process of learning the behavior of the data. Sequential learning is also often employed in this context in order to augment the feature vector of a given sample with information about its neighborhood. However, most part of works describe the samples using features obtained through standard arithmetic tools, which may not reflect the data as a whole. In this work, we introduced the Interval Arithmetic to the context of land-use classification in satellite images by describing a given sample and its neighbors using interval of values, thus allowing a better representation of the model. Experiments over four satellite images using two distinct supervised classifiers showed we can considerably improve sequential learning-oriented pattern classification using concepts from Interval Arithmetic. (C) 2016 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State Univ, Dept Comp, Bauru, SP, BrazilSao Paulo State Univ, Dept Comp, Bauru, SP, BrazilFAPESP: 2014/16250-9FAPESP: 2015/50319-9CNPq: 470571/2013-6CNPq: 306166/2014-3CNPq: 487032/2012-8Elsevier B.V.Universidade Estadual Paulista (Unesp)Pereira, Danillo Roberto [UNESP]Papa, Joao Paulo [UNESP]2018-11-26T17:10:28Z2018-11-26T17:10:28Z2016-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article188-194application/pdfhttp://dx.doi.org/10.1016/j.patrec.2016.03.020Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 83, p. 188-194, 2016.0167-8655http://hdl.handle.net/11449/16211710.1016/j.patrec.2016.03.020WOS:000386874800010WOS000386874800010.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPattern Recognition Letters0,662info:eu-repo/semantics/openAccess2024-04-23T16:11:00Zoai:repositorio.unesp.br:11449/162117Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:17:40.415277Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
title |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
spellingShingle |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification Pereira, Danillo Roberto [UNESP] Sliding Window Sequential learning Contextual learning Interval Arithmetic |
title_short |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
title_full |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
title_fullStr |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
title_full_unstemmed |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
title_sort |
A new approach to contextual learning using interval arithmetic and its applications for land-use classification |
author |
Pereira, Danillo Roberto [UNESP] |
author_facet |
Pereira, Danillo Roberto [UNESP] Papa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Papa, Joao Paulo [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Pereira, Danillo Roberto [UNESP] Papa, Joao Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Sliding Window Sequential learning Contextual learning Interval Arithmetic |
topic |
Sliding Window Sequential learning Contextual learning Interval Arithmetic |
description |
Contextual-based classification has been paramount in the last years, since spatial and temporal information play an important role during the process of learning the behavior of the data. Sequential learning is also often employed in this context in order to augment the feature vector of a given sample with information about its neighborhood. However, most part of works describe the samples using features obtained through standard arithmetic tools, which may not reflect the data as a whole. In this work, we introduced the Interval Arithmetic to the context of land-use classification in satellite images by describing a given sample and its neighbors using interval of values, thus allowing a better representation of the model. Experiments over four satellite images using two distinct supervised classifiers showed we can considerably improve sequential learning-oriented pattern classification using concepts from Interval Arithmetic. (C) 2016 Elsevier B.V. All rights reserved. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-11-01 2018-11-26T17:10:28Z 2018-11-26T17:10:28Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.patrec.2016.03.020 Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 83, p. 188-194, 2016. 0167-8655 http://hdl.handle.net/11449/162117 10.1016/j.patrec.2016.03.020 WOS:000386874800010 WOS000386874800010.pdf |
url |
http://dx.doi.org/10.1016/j.patrec.2016.03.020 http://hdl.handle.net/11449/162117 |
identifier_str_mv |
Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 83, p. 188-194, 2016. 0167-8655 10.1016/j.patrec.2016.03.020 WOS:000386874800010 WOS000386874800010.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pattern Recognition Letters 0,662 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
188-194 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129306937786368 |