Boosting Multi-Class SAR Oriented Object Detection via Geo-Topology-Guided Diffusion Synthesis

Oriented object detection in Synthetic Aperture Radar (SAR) imagery plays an important role in remote sensing, but its performance is usually limited by the shortage of high-quality annotated samples. This problem is particularly prominent in multi-class scenarios, where different targets exhibit significantly different scattering characteristics, scale distributions, orientation variations, and background dependencies. Existing SAR sample synthesis methods are mostly designed for single-category targets or horizontal bounding box constraints, and suffer from insufficient category diversity, weak orientation controllability, and inadequate modeling of geo-topological relationships. To address these problems, this paper proposes the first diffusion-based sample generation method for multi-class SAR oriented object detection. A large-scale SAR image-geo-topological semantic text paired dataset is constructed, and a SAR text-to-image foundation model is pretrained based on Stable Diffusion, enabling the model to learn target categories, quantities, spatial distributions, and geo-topological relationships, thereby improving the geographic plausibility and scene consistency of generated results. Furthermore, a Direction Phase Shifting encoding strategy is proposed to alleviate the boundary discontinuity problem in rotation-angle representation and to achieve precise control of target location, scale, and orientation based on oriented bounding boxes. Meanwhile, an automated sample generation and label refinement pipeline is designed. More accurate oriented bounding boxes that better fit target contours are obtained through wavelet denoising and progressive SAM-2 segmentation, improving the annotation accuracy of synthetic samples. Experimental results show that our method can produce SAR images with diverse scattering characteristics, realistic background variations, and reasonable geo-topological relationships. The generated samples consistently improve detection performance on five baseline oriented object detectors, providing an effective data augmentation strategy for enhancing the robustness and generalization capability of SAR oriented object detection models.