Source-Free Adaptation for Cross-Sensor SAR Target Detection via Prototype Anchoring and Foreground-Aware Manifold Constraint

Cross-sensor synthetic aperture radar (SAR) target detection faces significant domain shift challenges. In practical applications, source data are often inaccessible due to privacy, storage, and transmission constraints, making source-free domain adaptation (SFDA) an ideal solution. However, directly applying conventional SFDA methods to SAR images leads to severe performance degradation. Specifically, the strong clutter and discrete scattering characteristics of SAR targets induce semantic space drift, while severe geometric distortions destroy continuous visual contours, thereby leading to bounding box drift and degraded localization accuracy. To overcome these challenges, we propose a novel SFDA framework for cross-sensor SAR target detection by deeply mining the intrinsic physical invariants of SAR targets in the deep feature space. First, we design a Semantic Prototype Anchoring Calibration module to calibrate stable semantic space center. By leveraging inherent source model prototypes as semantic anchors, it effectively guides target-domain features to cluster correctly under strong clutter interference, thereby calibrating cross-domain semantic drift. Second, we introduce the Foreground-Aware Manifold Constraint (FAMC) module to regulate regression scattering distributions. By leveraging spatial centereness priors and mapping intrinsic foreground structures into a manifold space, FAMC jointly regularizes spatial centroids and interchannel geometries. This precisely aligns feature distributions, effectively mitigating localization degradation caused by geometric deformations. Experiments on cross-sensor tasks demonstrate that our framework reaches improvements of about 10%–25% in mean average precision and F1 score without accessing any source data, significantly enhancing the model’s generalization capability in unknown scenarios.