Scattering Center Prior-Guided Diffusion for Unknown-Azimuth SAR Image Generation

Abstract

Generating synthetic aperture radar (SAR) images at unknown azimuth angles under limited sample conditions remains a challenging task, since target scattering characteristics vary significantly with observation angle and are difficult to model effectively using only image-domain priors. To address this issue, this paper proposes a scattering center prior-guided conditional diffusion framework for unknown-azimuth SAR image generation. First, the Iterative Shrinkage–Thresholding Algorithm (ISTA) constrained by the Point Spread Function (PSF) is employed to extract dominant scattering centers from SAR images at known viewing angles, obtaining sparse and physically interpretable scattering center maps. Subsequently, the target category, azimuth angle, and scattering map are jointly used as conditional vector inputs to train the diffusion model. During the inference stage, scattering maps from known azimuth angles are fused to construct a scattering prior for the unknown azimuth, which is used to guide the generation of the corresponding SAR image. Experimental results under sparse angular sampling conditions demonstrate that, compared with the scattering-guided GAN baseline and the non-learning image-domain interpolation baseline, the proposed method better preserves dominant scattering structures and generates unknown-azimuth SAR images with clearer target contours, more stable strong scattering regions, and fewer local artifacts. In summary, introducing dominant scattering center priors into the conditional diffusion model provides effective physical constraints for SAR image generation and improves unseen-azimuth SAR image generation under the evaluated sparse angular sampling conditions.