Land use image classification through optimum-path forest clustering

Detalhes bibliográficos
Autor(a) principal: Pisani, R. [UNESP]
Data de Publicação: 2011
Outros Autores: Riedel, P. [UNESP], Ferreira, M. [UNESP], Marques, M. [UNESP], Mizobe, R. [UNESP], Papa, J. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IGARSS.2011.6049258
http://hdl.handle.net/11449/72802
Resumo: Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.
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spelling Land use image classification through optimum-path forest clusteringLand usemean shiftoptimum-path forestunsupervised classificationK-meansLanduse classificationsMean shiftUnsupervised classificationGeologyRemote sensingLand use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.UNESP - Univ. Estadual Paulista Geosciences and Exact Sciences InstituteUNESP - Univ. Estadual Paulista Department of ComputingUNESP - Univ. Estadual Paulista Geosciences and Exact Sciences InstituteUNESP - Univ. Estadual Paulista Department of ComputingUniversidade Estadual Paulista (Unesp)Pisani, R. [UNESP]Riedel, P. [UNESP]Ferreira, M. [UNESP]Marques, M. [UNESP]Mizobe, R. [UNESP]Papa, J. [UNESP]2014-05-27T11:26:07Z2014-05-27T11:26:07Z2011-11-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject826-829http://dx.doi.org/10.1109/IGARSS.2011.6049258International Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.http://hdl.handle.net/11449/7280210.1109/IGARSS.2011.6049258WOS:0002974963001992-s2.0-80955164075Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Geoscience and Remote Sensing Symposium (IGARSS)info:eu-repo/semantics/openAccess2021-10-23T21:37:45Zoai:repositorio.unesp.br:11449/72802Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:37:45Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Land use image classification through optimum-path forest clustering
title Land use image classification through optimum-path forest clustering
spellingShingle Land use image classification through optimum-path forest clustering
Pisani, R. [UNESP]
Land use
mean shift
optimum-path forest
unsupervised classification
K-means
Landuse classifications
Mean shift
Unsupervised classification
Geology
Remote sensing
title_short Land use image classification through optimum-path forest clustering
title_full Land use image classification through optimum-path forest clustering
title_fullStr Land use image classification through optimum-path forest clustering
title_full_unstemmed Land use image classification through optimum-path forest clustering
title_sort Land use image classification through optimum-path forest clustering
author Pisani, R. [UNESP]
author_facet Pisani, R. [UNESP]
Riedel, P. [UNESP]
Ferreira, M. [UNESP]
Marques, M. [UNESP]
Mizobe, R. [UNESP]
Papa, J. [UNESP]
author_role author
author2 Riedel, P. [UNESP]
Ferreira, M. [UNESP]
Marques, M. [UNESP]
Mizobe, R. [UNESP]
Papa, J. [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Pisani, R. [UNESP]
Riedel, P. [UNESP]
Ferreira, M. [UNESP]
Marques, M. [UNESP]
Mizobe, R. [UNESP]
Papa, J. [UNESP]
dc.subject.por.fl_str_mv Land use
mean shift
optimum-path forest
unsupervised classification
K-means
Landuse classifications
Mean shift
Unsupervised classification
Geology
Remote sensing
topic Land use
mean shift
optimum-path forest
unsupervised classification
K-means
Landuse classifications
Mean shift
Unsupervised classification
Geology
Remote sensing
description Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.
publishDate 2011
dc.date.none.fl_str_mv 2011-11-16
2014-05-27T11:26:07Z
2014-05-27T11:26:07Z
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.2011.6049258
International Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.
http://hdl.handle.net/11449/72802
10.1109/IGARSS.2011.6049258
WOS:000297496300199
2-s2.0-80955164075
url http://dx.doi.org/10.1109/IGARSS.2011.6049258
http://hdl.handle.net/11449/72802
identifier_str_mv International Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.
10.1109/IGARSS.2011.6049258
WOS:000297496300199
2-s2.0-80955164075
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 826-829
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
_version_ 1799965509052006400

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