Comparing Different Polarization Modes for Marine Oil Spills Classification Based on Complex Convolutional Neural Networks

Marine oil spills have caused serious harm to the costal ecological environment and marine economy. Synthetic aperture radar (SAR) has become a major equipment for oil spill detection because of its advantages of all-day and all-weather observation capability. In this paper, the complex convolutional neural network (CVCNN) framework is applied for marine oil spills classification. The classification performance of different polarization modes on marine oil spills classification is analyzed. Experimental results show that CP SAR modes have comparable performance as QP mode for marine oil spills classification. Among them, the Circular Transmit and Linear Receive (CTLR) mode has the best classification performance in the framework of CVCNN while DP (VVVH) mode is another promising alternative with much simpler hardware configuration requirement.