Oil spills identification in SAR image using mRMR and SVM model

in recent years, oil spills have been frequent and marine pollution has become increasingly serious. Synthetic Aperture Radar (SAR) satellites can effectively track the oil slicks expansion caused by the oil spill. The mRMR_SVM algorithm is used to identify oil spills in SAR images and the recognition results provide important preconditions for oil spill accident decision support. First, the algorithm of minimum redundancy and maximum relevance (mRMR) is applied to select the optimal eigenvector set, and the input values are reduced. Then the SVM algorithm is used to solve the oil spill image classification problem, and RBF is selected as the kernel function. Train the model using training sets and adjust the model parameters. The trained model is used to identify oil spills with the test set eigenvector as an input. The experimental results show that mRMR_SVM model are effective for the identification of "oil slicks" and "look-alikes oil slicks" image, and the accuracy rate is 96.875%.