Confusing and Challenging Negative Proposals Mining for Few-Shot SAR Ship Detection Based on Uncertainty Estimation

Deep-learning-based few-shot synthetic aperture radar (SAR) ship detection has demonstrated considerable potential in scenarios with limited annotated samples and dynamically emerging ship categories, closely matching the practical requirements of maritime surveillance and intelligent SAR image interpretation. However, existing methods often overlook challenging negative proposals and the incomplete annotation problem inherent in few-shot learning. In complex offshore and inshore scenes, negative proposals contain either difficult background regions caused by sea clutter, port facilities, and strong scattering interference, or unlabeled ship targets resulting from missing annotations. Existing methods struggle to distinguish between these two types of proposals. Ignoring challenging negatives prevents the detector from learning precise decision boundaries between ships and complex backgrounds, whereas treating unlabeled ships as background introduces erroneous gradients during backpropagation and degrades detection performance. To address these issues, we introduce uncertainty as a measure of proposal reliability and propose two complementary components: uncertainty-guided proposal separation (UGPS) and uncertainty-aware discriminative gradient refocusing (UADGR). UGPS jointly exploits proposal uncertainty and intersection-over-union (IoU) to separate challenging negatives and confusing negatives from the negative proposal set, thereby preserving informative hard backgrounds while identifying potential unlabeled ships. Subsequently, UADGR combines proposal uncertainty with feature dissimilarity to a background prototype to adaptively regulate their training gradients. Specifically, higher weights are assigned to challenging negatives to improve discrimination between ships and complex background interference, whereas lower weights are assigned to confusing negatives to suppress erroneous supervision introduced by missing ship annotations. Extensive experiments on SRSDD-v1.0 demonstrate consistent improvements over existing few-shot detection approaches across different data splits and shot settings, while additional results on SAR-AIRcraft-1.0 further confirm the generalization of the proposed method.