Hybrid Reasoning Network with Class-oriented Hierarchical Representation for Few-shot SAR Target Recognition

The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. Existing SAR ATR methods can achieve impressive results with a great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR target recognition under the condition of sample scarcity is a fundamental issue to be overcome urgently. In this paper, we put forward an ATR method named hybrid reasoning network with class-oriented hierarchical representation (HRNCHR) to achieve SAR target recognition with limited training samples. First, we develop a feature extraction model with local-global information concurrent refinement mechanism, which aims to simultaneously refine diverse features at both local and global levels from limited samples. Then, a hierarchical representation space that can learn hierarchical relationships between categories by utilizing diverse features learned from the feature extraction model is established, which is convenient to generalize to few-shot SAR target recognition tasks with the aid of category hierarchical relationship. Finally, a hybrid reasoning strategy is presented, which affords to fuse the reasoning results of instance-level and prototype-level to promote the accuracy of decision-making system. Extensive evaluation experiments on the publicly released moving and stationary target acquisition and recognition (MSTAR) dataset and OpenSARship dataset illustrate that the proposed method surpasses many state-of-the-art SAR ATR methods.