Ocean Eddy Recognition in SAR Images With Adaptive Weighted Feature Fusion

Automatic recognition of ocean eddies has become one of the hotspots in the field of physical oceanography. Traditional methods based on either physical parameters or geometry features require manual parameter adjustment, and cannot adapt to the dynamic changes of ocean eddies caused by complicated ocean environments. To address these issues, we propose a new eddy recognition method in SAR images with adaptive weighted multi-feature fusion. Specially, to better characterize eddies, we first extract texture, shape and corner features using global Gray Level Co-occurrence Matrix (GLCM), detailed Fourier Descriptor (FD) and local salient Harris features respectively. Secondly, considering the different importance of features for eddy recognition, we propose an adaptive weighted feature fusion method with multiple kernel learning (MKL). Here, a combined kernel is derived to fuse three selected kernels for the three types of features with the weights trained by MKL. Finally, we design a SVM classifier with the combined kernel to realize the eddy recognition. The experimental results show that: 1) our proposed method can reach 93.42% of eddy recognition accuracy, which is much higher than the methods with only one single feature; 2) adaptive weighted fusion plays an important role in improving the accuracy. Our proposed method with MKL gains a 8.36% accuracy increase than the method without MKL. Through adaptive weighted fusion, our method avoids the manual parameter adjustment and is more robust and general. Experimental results have proven that our method is effective and applicable to recognize eddies.