Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSchool of Mathematics and Statistics-
Autor(es): dc.creatorNegri, Rogerio G.-
Autor(es): dc.creatorAndrea Luz, E. O.-
Autor(es): dc.creatorFrery, Alejandro C.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T23:45:48Z-
Data de disponibilização: dc.date.available2025-08-21T23:45:48Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/MIGARS57353.2023.10064623-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/247074-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/247074-
Descrição: dc.descriptionThe frequency of forest fires has increased signifi- cantly in recent years across the planet. Events of this nature motivate the development of automated methodologies aimed at mapping areas affected by fire. In this context, we propose a method capable of accurately mapping areas affected by fire using time series of remotely sensed multispectral images by statistical modeling and classification. In order to evaluate the introduced proposal, we carry out a case study on a region in Brazil with recurrent history of forest fires. Furthermore, images obtained by the Landsat-8 satellite are used in this case study. Comparisons with an alternative method are included in this analysis.-
Descrição: dc.descriptionScience and Technology Institute São Paulo State University-
Descrição: dc.descriptionVictoria University of Wellington School of Mathematics and Statistics-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University-
Descrição: dc.descriptionScience and Technology Institute São Paulo State University-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relation2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023-
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Palavras-chave: dc.subjectForest fires-
Palavras-chave: dc.subjectmultitemporal-
Palavras-chave: dc.subjectspectral index-
Palavras-chave: dc.subjectunsupervised mapping-
Título: dc.titleFire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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