LGM-RNet: Large Margin Gaussian Mixture With Ring Loss Network for Imbalanced SAR Images Target Recognition

Convolutional neural networks (CNNs) have been widely employed in synthetic aperture radar (SAR) target recognition due to their powerful feature extraction capability. However, the performance of CNN-based SAR target recognition algorithms is often affected by imbalanced datasets, in which some classes own plenty of samples and some classes own few samples. To address this issue, this letter proposes a large-margin Gaussian mixture with a ring loss network (LGM-RNet). To improve CNN's recognition performance for classes with few samples, the algorithm clusters features of each class in the feature space and makes all the data to be equally distributed on a circle. Furthermore, to mitigate the impact of speckle noise in SAR images on target recognition, a denoising method based on Euclidean loss and the total variation loss is introduced. The proposed algorithm aims to improve the accuracy and robustness of imbalanced SAR image target recognition. Experimental results have verified the effectiveness of the proposed algorithm.