Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park.

Blondeau-Patissier, David; Schroeder, Thomas; Suresh, Gopika; Li, Zhibin; Diakogiannis, Foivos I; Irving, Paul; Witte, Christian; Steven, Andrew D L

Blondeau-Patissier, David; Schroeder, Thomas; Suresh, Gopika; Li, Zhibin; Diakogiannis, Foivos I; Irving, Paul; Witte, Christian; Steven, Andrew D L.

Afiliação

Blondeau-Patissier D; CSIRO Environment, Brisbane, Australia; Charles Darwin University, Darwin, Australia. Electronic address: david.blondeau-patissier@csiro.au.
Schroeder T; CSIRO Environment, Brisbane, Australia.
Suresh G; Unique land use GmbH, Freiburg, Germany.
Li Z; CSIRO Health and Biosecurity, Brisbane, Australia.
Diakogiannis FI; CSIRO Data61, Perth, Australia.
Irving P; Secretariat of the Pacific Regional Environment Programme (SPREP), Apia, Samoa.
Witte C; Department of Environment and Science (DES), Brisbane, Australia.
Steven ADL; CSIRO Environment, Brisbane, Australia.

Mar Pollut Bull ; 188: 114598, 2023 Mar.

Article em En | MEDLINE | ID: mdl-36773587

Assuntos

Aprendizado Profundo; Austrália; Europa (Continente); Monitoramento Ambiental/métodos

Palavras-chave

Automated detection; Deep learning; Great Barrier Reef; Oil pollution; SAR; Sentinel-1

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Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies País/Região como assunto: Europa / Oceania Idioma: En Revista: Mar Pollut Bull Ano de publicação: 2023 Tipo de documento: Article

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