Abstract

The generation of realistic synthetic aperture radar (SAR) images holds notable significance due to their applicability across various crucial domains in remote sensing, such as automatic target recognition and electronic countermeasures. The majority of current SAR image synthesis methods only leverage amplitude, thereby lacking the phase information that also plays an important role in SAR image interpretation. In this letter, we introduce Polarimetric Feature Guided Denoising Diffusion Probabilistic Model (PFG-DDPM), to generate PolSAR images. The proposed method can effectively simulate the distribution of real PolSAR images, encompassing both their amplitude and phase components. Importantly, we introduce an innovative strategy that employs polarimetric features as supervised information to guide the generation process of PolSAR images. This approach allows PFG-DDPM to effectively utilize constraints among distinct polarimetric channels, resulting in generated PolSAR images whose distributions closely approximate real PolSAR data. Experiments underscore the ability of the proposed method to produce realistic PolSAR images valid for human visual perception. More significantly, these images exhibit a remarkable resemblance to real PolSAR images, evidenced by a 44.4% and 5.3% enhancement in alignment with polarimetric and statistical attributes, respectively, compared to the vanilla DDPM.