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

@article{Matias2021ImprovedCM,
  title={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},
  author={{\'I}talo de Oliveira Matias and Patr{\'i}cia Carneiro Genovez and Sarah Barr{\'o}n Torres and Francisco F{\'a}bio de Ara{\'u}jo Ponte and Anderson Jos{\'e} Silva de Oliveira and Fernando Pellon de Miranda and Gil M{\'a}rcio Avellino},
  journal={Remote. Sens.},
  year={2021},
  volume={13},
  pages={4568}
}
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 develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using… 
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