RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/159557 |
Resumo: | The monitoring of water quality is essencial to the mankind, since we strongly depend on such resource for living and working. The presence of sediments in rivers usually indicates changes in the land use, which can affect the quality of water and the lifetime of hydroelectric power plants. In countries like Brazil, where more than 70% of the energy comes from the water, it is crucial to keep monitoring the sediment yield in rivers and lakes. In this work, we evaluate some state-of- the-art supervised pattern recognition techniques to classify different levels of sediments in Brazilian rivers using satellite images, as well as we make available an annotated dataset composed of two images to foster the related research. |
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RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERYSediment YieldMachine LearningOptimum-Path ForestThe monitoring of water quality is essencial to the mankind, since we strongly depend on such resource for living and working. The presence of sediments in rivers usually indicates changes in the land use, which can affect the quality of water and the lifetime of hydroelectric power plants. In countries like Brazil, where more than 70% of the energy comes from the water, it is crucial to keep monitoring the sediment yield in rivers and lakes. In this work, we evaluate some state-of- the-art supervised pattern recognition techniques to classify different levels of sediments in Brazilian rivers using satellite images, as well as we make available an annotated dataset composed of two images to foster the related research.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)SciTech - Science and Technology for Competitive and Sustainable IndustriesPrograma Operacional Regional do Norte (NORTE), through Fundo Europeu de Desenvolvimento Regional (FEDER)Univ Fed Alfenas, Nat Sci Inst, Alfenas, MG, BrazilSao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilUniv Porto, Fac Engn, Oporto, PortugalSao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilFAPESP: 2014/16250-9FAPESP: 2015/25739-4FAPESP: 2015/00801-9CNPq: 306166/2014-3SciTech - Science and Technology for Competitive and Sustainable Industries: NORTE-01-0145-FEDER-000022IeeeUniv Fed AlfenasUniversidade Estadual Paulista (Unesp)Univ PortoPisani, R.Costa, K. [UNESP]Rosa, G. [UNESP]Pereira, D. [UNESP]Papa, J. [UNESP]Tavares, J. M. R. S.IEEE2018-11-26T15:44:17Z2018-11-26T15:44:17Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs). New York: Ieee, 6 p., 2016.2377-0198http://hdl.handle.net/11449/159557WOS:000402041100003Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs)info:eu-repo/semantics/openAccess2021-10-23T21:44:27Zoai:repositorio.unesp.br:11449/159557Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:27Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
title |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
spellingShingle |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY Pisani, R. Sediment Yield Machine Learning Optimum-Path Forest |
title_short |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
title_full |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
title_fullStr |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
title_full_unstemmed |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
title_sort |
RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY |
author |
Pisani, R. |
author_facet |
Pisani, R. Costa, K. [UNESP] Rosa, G. [UNESP] Pereira, D. [UNESP] Papa, J. [UNESP] Tavares, J. M. R. S. IEEE |
author_role |
author |
author2 |
Costa, K. [UNESP] Rosa, G. [UNESP] Pereira, D. [UNESP] Papa, J. [UNESP] Tavares, J. M. R. S. IEEE |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Fed Alfenas Universidade Estadual Paulista (Unesp) Univ Porto |
dc.contributor.author.fl_str_mv |
Pisani, R. Costa, K. [UNESP] Rosa, G. [UNESP] Pereira, D. [UNESP] Papa, J. [UNESP] Tavares, J. M. R. S. IEEE |
dc.subject.por.fl_str_mv |
Sediment Yield Machine Learning Optimum-Path Forest |
topic |
Sediment Yield Machine Learning Optimum-Path Forest |
description |
The monitoring of water quality is essencial to the mankind, since we strongly depend on such resource for living and working. The presence of sediments in rivers usually indicates changes in the land use, which can affect the quality of water and the lifetime of hydroelectric power plants. In countries like Brazil, where more than 70% of the energy comes from the water, it is crucial to keep monitoring the sediment yield in rivers and lakes. In this work, we evaluate some state-of- the-art supervised pattern recognition techniques to classify different levels of sediments in Brazilian rivers using satellite images, as well as we make available an annotated dataset composed of two images to foster the related research. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-11-26T15:44:17Z 2018-11-26T15:44:17Z |
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 |
2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs). New York: Ieee, 6 p., 2016. 2377-0198 http://hdl.handle.net/11449/159557 WOS:000402041100003 |
identifier_str_mv |
2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs). New York: Ieee, 6 p., 2016. 2377-0198 WOS:000402041100003 |
url |
http://hdl.handle.net/11449/159557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
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 |
|
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1803650346679009280 |