Feature Optimization of Marine Oil Spill Polarimetric SAR Based on RF-RFECV

Marine oil spills have caused serious damage to the ecosystem and the environment, and timely and accurate detection of oil spills on the ocean surface is of vital significance to the maintenance of the marine ecological balance and the protection of the environment. At present, in terms of oil spill detection, the oil spill feature selection method is artificially subjective, the feature utilisation efficiency is low, and the application of multiple different feature combinations is less involved. For this reason, a RF-RFECV marine oil spill feature intelligent preference strategy based on RF-RFECV is proposed. The polarisation features and texture features of Radarsat-2 images are extracted systematically to construct a multi-dimensional feature space. The algorithm autonomously calculates the differentiation weights of each feature in the categories of oil spill, suspected oil spill and seawater, and finally selects the 16/18-dimensional optimal feature combinations for Radarsat-2 data. The experimental results show that the use of the optimal features for oil spill detection effectively improves the accuracy of oil spill detection, reduces the cases of misdetection and omission, and fully proves the effectiveness of the algorithm.