Land-use classification using Finite Element Machines

Detalhes bibliográficos
Autor(a) principal: Pereira, D. R. [UNESP]
Data de Publicação: 2018
Outros Autores: Papa, J. P. [UNESP], Papa, L. P., Pisani, R. J.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IGARSS.2018.8519252
http://hdl.handle.net/11449/187546
Resumo: Satellite images have been used in a number of applications, both in the academy and in the industry. One critical purpose concerns the land-use classification, which aims at automatically identifying different land-use applications, which range from economy and environmental monitoring to resources planning. In this paper, we introduce a new machine learning technique called Finite Element Machines (FEMa) in the context of land-use classification using satellite images. We show that FEMa can obtain results that are comparable to some state-of-the-art techniques in the literature.
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spelling Land-use classification using Finite Element MachinesFinite element machinesLand-use classificationRemote sensingSatellite images have been used in a number of applications, both in the academy and in the industry. One critical purpose concerns the land-use classification, which aims at automatically identifying different land-use applications, which range from economy and environmental monitoring to resources planning. In this paper, we introduce a new machine learning technique called Finite Element Machines (FEMa) in the context of land-use classification using satellite images. We show that FEMa can obtain results that are comparable to some state-of-the-art techniques in the literature.Department of Computing São Paulo State University - UNESPDepartment of Health Faculty Southwest PaulistaInstitute of Natural Sciences University of AlfenasDepartment of Computing São Paulo State University - UNESPUniversidade Estadual Paulista (Unesp)Faculty Southwest PaulistaUniversity of AlfenasPereira, D. R. [UNESP]Papa, J. P. [UNESP]Papa, L. P.Pisani, R. J.2019-10-06T15:39:44Z2019-10-06T15:39:44Z2018-10-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject7316-7319http://dx.doi.org/10.1109/IGARSS.2018.8519252International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 7316-7319.http://hdl.handle.net/11449/18754610.1109/IGARSS.2018.85192522-s2.0-85064177102Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Geoscience and Remote Sensing Symposium (IGARSS)info:eu-repo/semantics/openAccess2021-10-23T19:28:26Zoai:repositorio.unesp.br:11449/187546Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:46:03.232095Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Land-use classification using Finite Element Machines
title Land-use classification using Finite Element Machines
spellingShingle Land-use classification using Finite Element Machines
Pereira, D. R. [UNESP]
Finite element machines
Land-use classification
Remote sensing
title_short Land-use classification using Finite Element Machines
title_full Land-use classification using Finite Element Machines
title_fullStr Land-use classification using Finite Element Machines
title_full_unstemmed Land-use classification using Finite Element Machines
title_sort Land-use classification using Finite Element Machines
author Pereira, D. R. [UNESP]
author_facet Pereira, D. R. [UNESP]
Papa, J. P. [UNESP]
Papa, L. P.
Pisani, R. J.
author_role author
author2 Papa, J. P. [UNESP]
Papa, L. P.
Pisani, R. J.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Faculty Southwest Paulista
University of Alfenas
dc.contributor.author.fl_str_mv Pereira, D. R. [UNESP]
Papa, J. P. [UNESP]
Papa, L. P.
Pisani, R. J.
dc.subject.por.fl_str_mv Finite element machines
Land-use classification
Remote sensing
topic Finite element machines
Land-use classification
Remote sensing
description Satellite images have been used in a number of applications, both in the academy and in the industry. One critical purpose concerns the land-use classification, which aims at automatically identifying different land-use applications, which range from economy and environmental monitoring to resources planning. In this paper, we introduce a new machine learning technique called Finite Element Machines (FEMa) in the context of land-use classification using satellite images. We show that FEMa can obtain results that are comparable to some state-of-the-art techniques in the literature.
publishDate 2018
dc.date.none.fl_str_mv 2018-10-31
2019-10-06T15:39:44Z
2019-10-06T15:39:44Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IGARSS.2018.8519252
International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 7316-7319.
http://hdl.handle.net/11449/187546
10.1109/IGARSS.2018.8519252
2-s2.0-85064177102
url http://dx.doi.org/10.1109/IGARSS.2018.8519252
http://hdl.handle.net/11449/187546
identifier_str_mv International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 7316-7319.
10.1109/IGARSS.2018.8519252
2-s2.0-85064177102
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Geoscience and Remote Sensing Symposium (IGARSS)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 7316-7319
dc.source.none.fl_str_mv Scopus
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
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