2021
DOI: 10.3390/rs13224568
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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

Abstract: 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 synthetic aperture radar… Show more

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Cited by 7 publications
(7 citation statements)
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“…They are commonly used along with radiometric and textural features to distinguish oil slicks from false alarms [34,38,[62][63][64], or to extract dark spots from the ocean [35,38,39]. However, their use to discriminate seepage slicks from oil spills is recent [2,22,[42][43][44].…”
Section: Oil Slick Databasementioning
confidence: 99%
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“…They are commonly used along with radiometric and textural features to distinguish oil slicks from false alarms [34,38,[62][63][64], or to extract dark spots from the ocean [35,38,39]. However, their use to discriminate seepage slicks from oil spills is recent [2,22,[42][43][44].…”
Section: Oil Slick Databasementioning
confidence: 99%
“…Particularly, the use of SAR data to detect likely seepage slicks on sea surfaces represents an important instrument for reducing exploratory risk [4,5,22], as well as for protecting petroleum companies against penalties by events, not human-induced [22]. The location and persistence of detected slicks can be correlated with inverse oil drifting models [23,24], 3D-seismic and other remotely sensed geophysical data increasing confidence and strengthening evidence of active petroleum systems.…”
Section: Introductionmentioning
confidence: 99%
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“…for oil spill detection and discrimination for look alikes [10,31,36,37]. The researcher in [38] evaluated the impact of 6 machine learning approaches such as ANN, RF, Decision Tree, Navie Bayes, LDA, Logistic Regression for effective oil spill detection and develops an algorithm for prediction of the best season for image acquisition of oil spill for Gulf of Mexico region. Among all traditional approaches, ANN and SVM have been majorly used by researchers for oil spill detection and characterization.…”
Section: State-of-the-artmentioning
confidence: 99%