Few-Shot SAR Object Detection with Prior Class Perceptron and Cross-Entropy

Few-Shot Synthetic Aperture Radar (SAR) object detection aims to identify and localize unseen categories using only a small number of annotated support samples. However, performance is fundamentally constrained by two coupled challenges: speckle noise and structured clutter cause unstable region proposals and support–query mismatch, and extreme data scarcity leads to prototype drift and miscalibrated decision boundaries for novel classes. To tackle these issues in a coordinated framework, we propose Prior Class Perceptron Network (PCPNet), a support-conditioned detection framework that jointly improves proposal generation, the Prior Class Perceptron (PCP). First, the Adaptive Dense Proposal Module (ADPM) is designed to replace hard region selection with soft region-aware masks and uncertainty-aware mixture perception, enabling tighter, more reliable proposals in cluttered SAR scenes. Second, the PCP is utilized to decode prompted class perceptron from sparse supports, together with a self-calibrated prototype refinement strategy to mitigate prototype drift and stabilize novel scoring. Finally, by incorporating shot-dependent and uncertainty-aware adaptive margins into the Cross-Entropy (CE) loss, the PCPNet model improves inter-class separation and intra-class compactness under limited supervision. Extensive experiments on SAR-AIRCRAFT-1.0 and MSTAR-FEWSHOT demonstrate that PCPNet consistently achieves SOTA performance across three novel splits and multiple shot settings. In particular, PCPNet attains 27.47% mAP on SAR-AIRCRAFT-1.0 and 52.86% mAP on MSTAR-FEWSHOT under the single-run protocol, while also exhibiting strong robustness and cross-dataset generalization. Additional diagnostic, sensitivity, and qualitative analyses further verify the effectiveness and robustness of the proposed framework. These results indicate that jointly modeling uncertainty, prior class knowledge, and shot-dependent decision calibration provides an effective solution for few-shot SAR object detection.