Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) a...

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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 6; p. 1496
Main Authors:	Genovez, Patrícia Carneiro, Ponte, Francisco Fábio de Araújo, Matias, Ítalo de Oliveira, Torres, Sarah Barrón, Beisl, Carlos Henrique, Mano, Manlio Fernandes, Silva, Gil Márcio Avelino, Miranda, Fernando Pellon de
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-03-2023
Subjects:	Algorithms Artificial intelligence Continental margins Datasets Decision making Domains Human influences Knowledge Machine learning oceanic monitoring Oil pollution Oil slicks Oil spills Pattern recognition Petroleum industry Prediction models predictive models remote sensing Samples Seepage Sensors Statistical analysis Synthetic aperture radar Training Transfer learning Brazil Gulf of Mexico
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Summary:	The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (DS) follow the same statistical distribution of unknown samples to be predicted in the target domain (DT). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the DS and DT domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the DS and DT domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution.
ISSN:	2072-4292 2072-4292
DOI:	10.3390/rs15061496