Adaptive Contrastive Metric Network with Background Suppression for Few-Shot SAR Target Recognition

Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from overfitting under few-shot conditions, making it difficult to fully exploit the discriminative features contained in limited samples. Moreover, SAR targets frequently exhibit highly similar background scattering patterns, which further increase intra-class variations and reduce inter-class separability, thereby constraining the performance of few-shot recognition. To address these challenges, this paper proposes an adaptive contrastive metric (ACM) network with background suppression for few-shot SAR target recognition. Specifically, a spatial squeeze-and-excitation (SSE) attention module is introduced to adaptively highlight salient scattering structures of the target while effectively suppressing noise and irrelevant background interference, thus enhancing the robustness of feature representation. In addition, an ACM module is designed, where query samples are compared not only with their corresponding support class but also with the remaining classes. This enables explicit suppression of confusing background features and enlarges inter-class margins, thereby improving the discriminability of the learned feature space. The experimental results on publicly available SAR target recognition datasets demonstrate that the proposed method achieves significant improvements in background suppression and consistently outperforms several state-of-the-art metric-based few-shot learning approaches, validating the effectiveness and generalizability of the proposed framework.