Fine-Grained Continual Learning for SAR Target Recognition

Synthetic Aperture Radar (SAR) systems work in the open and dynamic environment, and capture new data or new targets continually over time. It requires that SAR target recognition algorithms should have the capability to learn new targets incrementally without forgetting the previously learned targets. Besides, SAR targets of different classes usually have subtle differences which makes the recognition more challenging. In this paper, we propose a fine-grained continual learning algorithm for SAR incremental target recognition. Since data imbalance between old and new classes results in the forgetting of old classes, class-balanced loss is introduced to alleviate this phenomenon. In addition, covariance pooling network is utilized to explore the higher-order statistical information to improve the discrimination of features. Experimental results on real SAR datasets validate the effectiveness of the proposed method.