Marine Oil Spills Detection and Classification from Polsar Images Based on Complex-Valued Convolutional Neural Network

Abstract

The recent development of marine transportation and offshore oil exploration and exploitation has increased the risk of marine oil spill accidents. Oil pollution is one of the most complex marine pollutions, it will seriously threaten the marine ecological environment. Synthetic aperture radar (SAR) has been widely used in marine monitoring for its all-day and all-weather imaging capability. Polarimetric SAR can obtain polarimetric scattering information of the ground targets, which provides a new means for marine oil spill detection. Besides, with the recent development of machine learning algorithms, especially convolutional neural networks, higher oil spill classification accuracy can be obtained given more training datasets. However, currently most neural network models are applied to real-valued input and cannot fully exploit the phase information contained in complex-valued data of polarimetric SAR images. In this study, a classification approach of marine oil spills in polarimetric SAR images is presented based on the complex-valued convolutional neural network (CVCNN). The experimental results show that the proposed approach outperforms the real-valued convolutional neural network (RVCNN) in both the detection of oil spills from sea surface and the classification of crude oil films and biogenic oil films.