An incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorFederal University of Itajuba-
Autor(es): dc.contributorScience and Technology of Mato Grosso (IFMT)-
Autor(es): dc.creatorDias, Maurcio Arajo [UNESP]-
Autor(es): dc.creatorda Silva, Erivaldo Antnio [UNESP]-
Autor(es): dc.creatorde Azevedo, Samara Calado-
Autor(es): dc.creatorCasaca, Wallace [UNESP]-
Autor(es): dc.creatorStatella, Thiago-
Autor(es): dc.creatorNegri, Rogrio Galante [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:29:20Z-
Data de disponibilização: dc.date.available2022-02-22T00:29:20Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/RS12010043-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/200083-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/200083-
Descrição: dc.descriptionThe potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determinehowanomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.-
Descrição: dc.descriptionDepartment of Mathematics and Computer Science School of Sciences and Technology So Paulo State University (UNESP), Campus Presidente Prudente-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology So Paulo State University (UNESP), Campus Presidente Prudente-
Descrição: dc.descriptionNatural Resources Department Federal University of Itajuba, Av. BPS 1303-
Descrição: dc.descriptionDepartment of Energy Engineering So Paulo State University (UNESP), Campus Rosana-
Descrição: dc.descriptionFederal Institute of Education Science and Technology of Mato Grosso (IFMT), 95 Zulmira Canavarro-
Descrição: dc.descriptionDepartment of Environmental Engineering Sciences and Technology Institute So Paulo State University (UNESP), Campus So Jos dos Campos-
Descrição: dc.descriptionDepartment of Mathematics and Computer Science School of Sciences and Technology So Paulo State University (UNESP), Campus Presidente Prudente-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology So Paulo State University (UNESP), Campus Presidente Prudente-
Descrição: dc.descriptionDepartment of Energy Engineering So Paulo State University (UNESP), Campus Rosana-
Descrição: dc.descriptionDepartment of Environmental Engineering Sciences and Technology Institute So Paulo State University (UNESP), Campus So Jos dos Campos-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAnalysis of images pattern recognition-
Palavras-chave: dc.subjectAnomaly detection-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectIncongruence-
Palavras-chave: dc.subjectRemote sensing-
Título: dc.titleAn incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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