An Azimuth Aware Deep Reinforcement Learning Framework for Active SAR Target Recognition

Abstract

Achieving the automatic target recognition in synthetic aperture radar (SAR) imagery is a long-standing difficulty because of the limited training samples and its sensitivity to imaging condition. Active target recognition methods can offer an innovative perspective to improve the recognition accuracy compared to its passive counterpart. Although prevailing in the optical imagery area, the active target recognition in the SAR image processing remains under-explored. This paper proposes an active SAR target recognition framework based on deep reinforcement learning for the first time, where we design a simple view-matching task and model it as a Markov decision process. The proximal policy optimization algorithm is used to help the agent learn how to alter the observing azimuth to seek more discriminative target images for the classifier. Furthermore, the single-view feature extractor is trained with the contrastive learning method to help distinguish the target images under different azimuths, allowing the agent to successfully learn the active data collection policy in the training environment and transfer it to the test environment. Lastly, the effectiveness and advancement of the proposed framework are verified on the SAMPLE dataset. When the training samples for the classifier are very scarce, it could bring around 10 percent more gain in target recognition rate compared to existing active target recognition frameworks.