Rotation Awareness Based Self-Supervised Learning for SAR Target Recognition

In this paper, we newly suggest that more attention should be paid on learning rotation-equivariant and label-invariant features for each target instead of the conventional rotation-invariant ones. To achieve this goal, we present a novel rotation awareness based self-supervised learning (RR-SSL) deep model to recognize the behavior of target rotation, which is also benefit from the discriminative training scheme without manual labeling. Then this model is incorporated into another deep discriminative model of target recognition to form a dual-task learning framework, where their bottom layers are shared to capture the expected features. Sufficient experimental results on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of our proposed model. The overall framework can achieve a better or comparative recognition accuracy compared with other state-of-the-art SAR-ATR algorithms.