Oil spill detection using refined convolutional neural network based on quad-polarimetric SAR images

Quad-polarimetric SAR data has been proved to be useful for marine oil spill classification. Different SAR polarimetric features have been proposed to discriminate between oil spills and look-alikes which could cause false detection. In this paper we explored the ability of convolutional neural network (CNN) in automatic oil spill classification, by taking the advantage of H/A/Alpha polarimetric decomposition features and co-polarized correlation coefficients(CC). The convolutional neural network (CNN) was refined to realize the classification, in which global average pooling layer is applied instead of full connection layer. The quad-polarimetric Radarsat-2 data acquired during the Norwegian oil-on-water exercise was tested in the experiment. Sea surface was classified as clean sea, oil spill, look-alikes(biological oil spill in this case), and emulsion. The experiment results show that H/A/Alpha parameters and the combination of H/A/Alpha and co-polarized CC obtained higher accuracy, and the refined CNN has better performance than the traditional one in terms of accuracy and efficiency.