STARS-SAR: Structure-Aware Diffusion for Detector-Oriented SAR Ship Image Augmentation
Synthetic aperture radar (SAR) ship detection is often limited by the quantity and distribution coverage of labeled training data. Under this condition, SAR ship image generation for augmentation should not be treated as a purely visual synthesis problem. For downstream detector training, the generated samples should preserve controllable target layout, SAR-domain compatibility, and sufficient consistency with real SAR observations. Existing methods, however, still have difficulty satisfying these requirements jointly, which limits the usefulness of synthetic samples for SAR ship detection. To address this issue, this article proposes STARS-SAR, a structure-aware diffusion framework for detector-oriented SAR ship augmentation. Instead of treating structural controllability and SAR-domain adaptation as separate objectives, STARS-SAR integrates them into a unified generation process through structure-guided cross-attention modulation, an SAR-oriented LoRA adaptation strategy, and region-aware SAR texture alignment. In this way, the generated samples remain more compatible with real SAR observations while preserving local target–background coherence. Experiments on HRSID and SSDD show that STARS-SAR generates representative SAR ship images with improved structural fidelity and better compatibility with real SAR observations. Under limited-data training settings, the generated samples also serve as effective augmentation data and improve downstream ship detection in both in-domain and cross-domain settings. These results show that STARS-SAR is effective for detector-oriented SAR ship image augmentation under practical data-limited conditions.