A case study for a multitemporal segmentation approach in optical remote sensing images.
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1090733 |
Resumo: | Continuous observations from remote sensors provide high temporal and spatial resolution imagery, and better remote sensing image segmentation techniques are mandatory for efficient analysis. Among them, one of the most applied segmentation techniques is the region growing algorithm. Within this context, this paper describes a study case for a multitemporal segmentation that adapts the traditional region growing technique. Our method aims to detect homogeneous regions in space and time observing a sequence of optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping distance as the homogeneity criterion to grow regions. A case study on high temporal resolution for sequences of Landsat-8 vegetation indices products provided satisfactory outputs. |
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A case study for a multitemporal segmentation approach in optical remote sensing images.Segmentação multitemporalDynamic Time WarpingProcessamento de imagemSistema de informação geográficaSensoriamento remotoContinuous observations from remote sensors provide high temporal and spatial resolution imagery, and better remote sensing image segmentation techniques are mandatory for efficient analysis. Among them, one of the most applied segmentation techniques is the region growing algorithm. Within this context, this paper describes a study case for a multitemporal segmentation that adapts the traditional region growing technique. Our method aims to detect homogeneous regions in space and time observing a sequence of optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping distance as the homogeneity criterion to grow regions. A case study on high temporal resolution for sequences of Landsat-8 vegetation indices products provided satisfactory outputs.GEOProcessing 2018.WANDERSON COSTA, INPE; LEILA FONSECA, INPE; THALES KÖRTING, INPE; MARGARETH GONCALVES SIMOES, CNPS; PATRICK KUCHLER, UERJ; CIRAD.COSTA, W.FONSECA, L.KÖRTING, T.SIMÕES, M.KUCHLER, P.2018-04-20T01:10:39Z2018-04-20T01:10:39Z2018-04-1920182019-04-16T11:11:11ZArtigo em anais e proceedingsinfo:eu-repo/semantics/publishedVersionIn: INTERNATIONAL CONFERENCE ON ADVANCED GEOGRAPHIC INFORMATION SYSTEMS, APPLICATIONS, AND SERVICES, 10., 2018, Rome. Proceedings... Haifa: Israel Institute of Technology, 2018. p. 66-70.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1090733enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-04-20T01:10:46Zoai:www.alice.cnptia.embrapa.br:doc/1090733Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-04-20T01:10:46Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
title |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
spellingShingle |
A case study for a multitemporal segmentation approach in optical remote sensing images. COSTA, W. Segmentação multitemporal Dynamic Time Warping Processamento de imagem Sistema de informação geográfica Sensoriamento remoto |
title_short |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
title_full |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
title_fullStr |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
title_full_unstemmed |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
title_sort |
A case study for a multitemporal segmentation approach in optical remote sensing images. |
author |
COSTA, W. |
author_facet |
COSTA, W. FONSECA, L. KÖRTING, T. SIMÕES, M. KUCHLER, P. |
author_role |
author |
author2 |
FONSECA, L. KÖRTING, T. SIMÕES, M. KUCHLER, P. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
WANDERSON COSTA, INPE; LEILA FONSECA, INPE; THALES KÖRTING, INPE; MARGARETH GONCALVES SIMOES, CNPS; PATRICK KUCHLER, UERJ; CIRAD. |
dc.contributor.author.fl_str_mv |
COSTA, W. FONSECA, L. KÖRTING, T. SIMÕES, M. KUCHLER, P. |
dc.subject.por.fl_str_mv |
Segmentação multitemporal Dynamic Time Warping Processamento de imagem Sistema de informação geográfica Sensoriamento remoto |
topic |
Segmentação multitemporal Dynamic Time Warping Processamento de imagem Sistema de informação geográfica Sensoriamento remoto |
description |
Continuous observations from remote sensors provide high temporal and spatial resolution imagery, and better remote sensing image segmentation techniques are mandatory for efficient analysis. Among them, one of the most applied segmentation techniques is the region growing algorithm. Within this context, this paper describes a study case for a multitemporal segmentation that adapts the traditional region growing technique. Our method aims to detect homogeneous regions in space and time observing a sequence of optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping distance as the homogeneity criterion to grow regions. A case study on high temporal resolution for sequences of Landsat-8 vegetation indices products provided satisfactory outputs. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-20T01:10:39Z 2018-04-20T01:10:39Z 2018-04-19 2018 2019-04-16T11:11:11Z |
dc.type.driver.fl_str_mv |
Artigo em anais e proceedings |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
In: INTERNATIONAL CONFERENCE ON ADVANCED GEOGRAPHIC INFORMATION SYSTEMS, APPLICATIONS, AND SERVICES, 10., 2018, Rome. Proceedings... Haifa: Israel Institute of Technology, 2018. p. 66-70. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1090733 |
identifier_str_mv |
In: INTERNATIONAL CONFERENCE ON ADVANCED GEOGRAPHIC INFORMATION SYSTEMS, APPLICATIONS, AND SERVICES, 10., 2018, Rome. Proceedings... Haifa: Israel Institute of Technology, 2018. p. 66-70. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1090733 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
_version_ |
1822721323154014208 |