ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]

This paper presents a new approach for detecting algal insurgence in water environments by using remote sensing image series. The designed methodology provides a robust and accurate algorithm as an alternative to typical algal bloom detection methods. In more technical terms, by only assuming as inp...

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Detalles Bibliográficos
Autores: Ananias, Pedro Henrique M. [UNESP], Negri, Rogério G. [UNESP], Bressane, Adriano [UNESP], Colnago, Marilaine [UNESP], Casaca, Wallace [UNESP]
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:Brasil
Institución:Universidade Estadual Paulista (UNESP)
Repositorio:Repositório Institucional da UNESP
Idioma:inglés
OAI Identifier:oai:repositorio.unesp.br:11449/248380
Acceso en línea:http://dx.doi.org/10.1016/j.simpa.2023.100482
http://hdl.handle.net/11449/248380
Access Level:acceso abierto
Palabra clave:Algal bloom
Machine learning
Remote sensing
Spectral index
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spelling ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]Algal bloomMachine learningRemote sensingSpectral indexThis paper presents a new approach for detecting algal insurgence in water environments by using remote sensing image series. The designed methodology provides a robust and accurate algorithm as an alternative to typical algal bloom detection methods. In more technical terms, by only assuming as input an image time series, a fully automatic data-driven scheme involving pre-processing and feature extraction procedures is derived, which models a machine intelligent-based classifier capable of detecting algal blooms. Lastly, algal insurgence maps are then produced by passing to the classifier an image taken at an instant of interest.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University (UNESP), São José dos CamposGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos CamposCivil and Environmental Engineering Graduate Program (UNESP), BauruSão Paulo State University (UNESP), AraraquaraSão Paulo State University (UNESP), São José do Rio PretoSão Paulo State University (UNESP), São José dos CamposGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos CamposCivil and Environmental Engineering Graduate Program (UNESP), BauruSão Paulo State University (UNESP), AraraquaraSão Paulo State University (UNESP), São José do Rio PretoFAPESP: 2021/01305-6FAPESP: 2021/03328-3CNPq: 316228/2021-4Universidade Estadual Paulista (UNESP)2023-07-29T13:42:28Z2023-07-29T13:42:28Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.simpa.2023.100482Software Impacts, v. 15.2665-9638http://hdl.handle.net/11449/24838010.1016/j.simpa.2023.1004822-s2.0-85148354188Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSoftware Impactsinfo:eu-repo/semantics/openAccessAnanias, Pedro Henrique M. [UNESP]Negri, Rogério G. [UNESP]Bressane, Adriano [UNESP]Colnago, Marilaine [UNESP]Casaca, Wallace [UNESP]2025-08-29T05:02:33Zoai:repositorio.unesp.br:11449/248380Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-08-29T05:02:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
title ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
spellingShingle ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
Ananias, Pedro Henrique M. [UNESP]
Algal bloom
Machine learning
Remote sensing
Spectral index
title_short ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
title_full ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
title_fullStr ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
title_full_unstemmed ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
title_sort ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
dc.creator.none.fl_str_mv Ananias, Pedro Henrique M. [UNESP]
Negri, Rogério G. [UNESP]
Bressane, Adriano [UNESP]
Colnago, Marilaine [UNESP]
Casaca, Wallace [UNESP]
author Ananias, Pedro Henrique M. [UNESP]
author_facet Ananias, Pedro Henrique M. [UNESP]
Negri, Rogério G. [UNESP]
Bressane, Adriano [UNESP]
Colnago, Marilaine [UNESP]
Casaca, Wallace [UNESP]
author_role author
author2 Negri, Rogério G. [UNESP]
Bressane, Adriano [UNESP]
Colnago, Marilaine [UNESP]
Casaca, Wallace [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.subject.por.fl_str_mv Algal bloom
Machine learning
Remote sensing
Spectral index
topic Algal bloom
Machine learning
Remote sensing
Spectral index
description This paper presents a new approach for detecting algal insurgence in water environments by using remote sensing image series. The designed methodology provides a robust and accurate algorithm as an alternative to typical algal bloom detection methods. In more technical terms, by only assuming as input an image time series, a fully automatic data-driven scheme involving pre-processing and feature extraction procedures is derived, which models a machine intelligent-based classifier capable of detecting algal blooms. Lastly, algal insurgence maps are then produced by passing to the classifier an image taken at an instant of interest.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:42:28Z
2023-07-29T13:42:28Z
2023-03-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.simpa.2023.100482
Software Impacts, v. 15.
2665-9638
http://hdl.handle.net/11449/248380
10.1016/j.simpa.2023.100482
2-s2.0-85148354188
url http://dx.doi.org/10.1016/j.simpa.2023.100482
http://hdl.handle.net/11449/248380
identifier_str_mv Software Impacts, v. 15.
2665-9638
10.1016/j.simpa.2023.100482
2-s2.0-85148354188
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Software Impacts
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 repositoriounesp@unesp.br
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score 15,300719

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