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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Remote Sensing
Subjects:
synthetic aperture radar (SAR)
machine learning (ML)
exploratory data analysis (EDA)
classification model (CM)
oil slicks source (OSS)
oil seeps
Online Access:https://www.mdpi.com/2072-4292/13/22/4568

Internet

https://www.mdpi.com/2072-4292/13/22/4568

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