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

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dc.contributor.authorAnanias, Pedro Henrique M. [UNESP]
dc.contributor.authorNegri, Rogério G. [UNESP]
dc.contributor.authorBressane, Adriano [UNESP]
dc.contributor.authorColnago, Marilaine [UNESP]
dc.contributor.authorCasaca, Wallace [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:42:28Z
dc.date.available2023-07-29T13:42:28Z
dc.date.issued2023-03-01
dc.description.abstractThis 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.en
dc.description.affiliationSão Paulo State University (UNESP), São José dos Campos
dc.description.affiliationGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos
dc.description.affiliationCivil and Environmental Engineering Graduate Program (UNESP), Bauru
dc.description.affiliationSão Paulo State University (UNESP), Araraquara
dc.description.affiliationSão Paulo State University (UNESP), São José do Rio Preto
dc.description.affiliationUnespSão Paulo State University (UNESP), São José dos Campos
dc.description.affiliationUnespGraduate Program in Natural Disasters (UNESP/CEMADEN), São José dos Campos
dc.description.affiliationUnespCivil and Environmental Engineering Graduate Program (UNESP), Bauru
dc.description.affiliationUnespSão Paulo State University (UNESP), Araraquara
dc.description.affiliationUnespSão Paulo State University (UNESP), São José do Rio Preto
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2021/01305-6
dc.description.sponsorshipIdFAPESP: 2021/03328-3
dc.description.sponsorshipIdCNPq: 316228/2021-4
dc.identifierhttp://dx.doi.org/10.1016/j.simpa.2023.100482
dc.identifier.citationSoftware Impacts, v. 15.
dc.identifier.doi10.1016/j.simpa.2023.100482
dc.identifier.issn2665-9638
dc.identifier.scopus2-s2.0-85148354188
dc.identifier.urihttp://hdl.handle.net/11449/248380
dc.language.isoeng
dc.relation.ispartofSoftware Impacts
dc.sourceScopus
dc.subjectAlgal bloom
dc.subjectMachine learning
dc.subjectRemote sensing
dc.subjectSpectral index
dc.titleABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]en
dc.typeArtigo
unesp.author.orcid0000-0002-4808-2362 0000-0002-4808-2362[2]

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Artigos - Engenharia Civil e Ambiental - FEB