Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to deve...

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Autores principales:	Ítalo de Oliveira Matias, Patrícia Carneiro Genovez, Sarah Barrón Torres, Francisco Fábio de Araújo Ponte, Anderson José Silva de Oliveira, Fernando Pellon de Miranda, Gil Márcio Avellino
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	synthetic aperture radar (SAR) machine learning (ML) exploratory data analysis (EDA) classification model (CM) oil slicks source (OSS) oil seeps Science Q
Acceso en línea:	https://doaj.org/article/10e960cf8b374cba9e7883325556fc1d
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Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

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