ConFAS-Net: Few-Shot SAR Target Recognition via Confusion-Aware Attention and Adaptive Decision Scaling

Synthetic aperture radar (SAR) target recognition under few-shot scenarios faces challenges of insufficient feature extraction and severe inter-class confusion. To address these issues, a confusion-aware few-shot attention and scaling network (ConFAS-Net) is proposed. The method introduces a multi-scale channel attention module (MS-CA) to enhance adaptive extraction of multi-scale features, designs a confusion-aware loss optimization module (CACL) to guide discriminative feature learning using inter-class confusion information, and employs a class-adaptive decision adjustment module (CADA) to dynamically adjust classification boundaries for few-shot distribution characteristics. Extensive experiments on the standard MSTAR dataset demonstrated that ConFAS-Net achieved recognition accuracies of 73.25%, 87.43%, 94.97%, and 96.87% under 5-, 10-, 15-, and 30-shot settings, respectively. To rigorously substantiate the generalization capability and robustness of the proposed model across different data domains, additional validation was conducted on the public SAMPLE dataset, where ConFAS-Net consistently achieved state-of-the-art performance across all K-shot settings. Ablation studies and visualization analyses further validated the effectiveness of each proposed module. Comparisons with state-of-the-art methods demonstrate that the proposed method maintains high recognition accuracy while retaining a lightweight architecture of only 2.32 M parameters, providing an effective solution for SAR target recognition in resource-constrained environments.