Physics-Guided Synthetic Aperture Radar Image Generation and Target Detection Enhancement Framework

Synthetic Aperture Radar (SAR) image generation methodologies exhibit geometric distortions and scattering feature degradation in cross-angle scenarios, with insufficient ability for target detection applications. To address this limitation, we propose a Physics-guided Generative-Detection Joint Framework (PhysGAN-Det). Specifically, we design a Physics-guided Multiscale Attention module (PMA) that dynamically enhances scattering structures through depression angle-conditioned encoding and multiscale convolutional fusion. Furthermore, an Attribute Scattering Center (ASC) loss function is developed to enforce scattering feature consistency through physical scattering modeling. Therefore, a generative data augmentation framework is established to improve cross-angle generalization of target detection by expanding datasets with multi-angle synthetic image. Extensive experiments on the MSTAR and OpenSARShip benchmark demonstrate the superior performance of generated images. For target detection, the proposed framework improves mean Average Precision (mAP) by 8%-10.7% across diverse detection architectures. This approach provides a physically interpretable and high-precision data augmentation solution for SAR target detection and recognition.