Variable-complexity machine learning models for large-scale oil spill detection: The case of Persian Gulf

Oil spill is the main cause of marine pollution in the waterbodies with rich oil resources. In this study, we developed and compared the performance of variable-complexity machine-learning models to detect oil spill origin, extent, and movement over large scales. To this end, we trained Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) models by using the statistical, geometrical, and textural features of Sentinel-1 SAR data. Our results in the Persian Gulf showed that CNN is superior to RF and SVM classifiers in oil spill detection, as evidenced by the testing accuracy of 95.8 %, 86.0 %, and 78.9 %, respectively. The results suggested utilizing both ascending and descending orbit pass directions to track the movement of oil spill and the underlying transport rate. The proposed methodology enables the detection of probable leaking tankers and platforms, which aids in identifying other sources of oil pollution than tankers and platforms.