Class Hierarchy Aware Contrastive Feature Learning for Multi-Granularity SAR Target Recognition

Deep learning has emerged as the dominant paradigm for Synthetic Aperture Radar (SAR) automatic target recognition (ATR). However, existing learning algorithms focus primarily on single-granularity categorical recognition task without fully exploiting the high-order correlations among both data and label domains. This paper presents a multi-granularity SAR ATR framework called class hierarchy aware contrastive feature learning. The framework consists of three modules designed to extract higher-order discriminative features, enforce hierarchical feature distribution, and enable hierarchically consistent classification. The feature extractor module extracts higher-order class-specific features from two groups of multi-view samples, breaking the assumption of i.i.d. data. Furthermore, we introduce a structured contrastive regularization module to enforce a hierarchical distribution in the feature domain, thereby improving the discriminative power of the learned representations and addressing the issue of hierarchical violation. Additionally, an effective multi-granularity label representation scheme and a hierarchical classifier are developed to leverage the hierarchical relationships among classes. The framework contributes to advancing SAR ATR by providing insights into hierarchical category-aware feature learning and classification, and improving the generalization ability in scenarios with limited training samples. Experimental evaluations conducted on a benchmark SAR dataset validate the effectiveness of the proposed framework. Particularly in scenarios with limited training samples and zero-shot ATR, our framework outperforms state-of-the-art methods, achieving better performance.