Supervised oil spill classification based on fully polarimetric SAR features

Oil spill has been a crucial hazard to the coastal environment. A major difficulty of Synthetic Aperture Radar (SAR) based oil-spill detection algorithms is the classification between mineral oil and biogenic look-alikes. Polarimetric SAR features provides helpful information in disguising mineral oil and its look-alikes. In this study, we focused on the extraction and selection of fully polarimetric SAR features for the classification between mineral and biogenic look-alikes. Three mainly used supervised classifiers including Support vector machine (SVM), Artificial neural network (ANN) and Maximum likelihood classification (ML) were comparatively studied. In the experiment, classification performance increases with the growth of the feature number initially, but still fluctuates or decreases after the sufficient features are considered. It was also discovered that among all the classifiers, support vector machine performed best.