River sediment yield classification using remote sensing imagery
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PRRS.2016.7867014 http://hdl.handle.net/11449/220822 |
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 stateof-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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Repositório Institucional da UNESP |
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2946 |
spelling |
River sediment yield classification using remote sensing imageryMachine LearningOptimum-Path ForestSediment YieldThe 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 stateof-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)Federal University of Alfenas Natural Sciences InstituteSao Paulo State University Department of ComputingUniversidade Do Porto Faculdade de EngenhariaSao Paulo State University Department of ComputingFAPESP: #2014/16250-9FAPESP: #2015/00801-9FAPESP: #2015/25739-4Natural Sciences InstituteUniversidade Estadual Paulista (UNESP)Universidade Do Porto Faculdade de EngenhariaPisani, R.Costa, K. [UNESP]Rosa, G. [UNESP]Pereira, D. [UNESP]Papa, J. [UNESP]Tavares, J. M.R.S.2022-04-28T19:05:59Z2022-04-28T19:05:59Z2017-02-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PRRS.2016.78670142016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016.http://hdl.handle.net/11449/22082210.1109/PRRS.2016.78670142-s2.0-85017005188Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016info:eu-repo/semantics/openAccess2022-04-28T19:05:59Zoai:repositorio.unesp.br:11449/220822Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:05:59Repositório 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. Machine Learning Optimum-Path Forest Sediment Yield |
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. |
author_role |
author |
author2 |
Costa, K. [UNESP] Rosa, G. [UNESP] Pereira, D. [UNESP] Papa, J. [UNESP] Tavares, J. M.R.S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Natural Sciences Institute Universidade Estadual Paulista (UNESP) Universidade Do Porto Faculdade de Engenharia |
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. |
dc.subject.por.fl_str_mv |
Machine Learning Optimum-Path Forest Sediment Yield |
topic |
Machine Learning Optimum-Path Forest Sediment Yield |
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 stateof-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 |
2017 |
dc.date.none.fl_str_mv |
2017-02-28 2022-04-28T19:05:59Z 2022-04-28T19:05:59Z |
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/PRRS.2016.7867014 2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016. http://hdl.handle.net/11449/220822 10.1109/PRRS.2016.7867014 2-s2.0-85017005188 |
url |
http://dx.doi.org/10.1109/PRRS.2016.7867014 http://hdl.handle.net/11449/220822 |
identifier_str_mv |
2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016. 10.1109/PRRS.2016.7867014 2-s2.0-85017005188 |
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 2016 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1803650334701125632 |