Scattering Mechanism Inspired Non-Gaussian Diffusion Model for Polarimetric SAR Image Classification

The diffusion model has achieved excellent performance in natural image processing, which can learn the noise distribution through the degradation and restoration processes. However, the model is limited to Gaussian noise. Actually, polarimetric synthetic aperture radar (PolSAR) images have complex non-Gaussian speckle noises, for which the Gaussian diffusion model is difficult to learn their intrinsic statistical characteristics. In this article, we propose a novel scattering mechanism inspired non-Gaussian diffusion model for PolSAR image classification. To better simulate the PolSAR speckle noise, a mixed noise distribution is defined for PolSAR covariance matrices by combining Gamma multiplicative and Gaussian additive noises. A non-Gaussian forward noising process is derived to degrade a clean PolSAR image to a noisy image in steps. Then, the U-Net structure is trained to remove noise for each step, effectively extracting non-Gaussian statistical features. However, statistical features can only characterize the overall distribution of the dataset, which is insufficient to describe complicated individual objects; the original PolSAR data reflect the detailed scattering mechanism for individual pixels, which can provide complementary object information for classification. Therefore, a scattering–statistical joint learning network is further developed with a dual-branch architecture to enhance the discrimination ability. In particular, a multiscale pyramid module and attention mechanism are designed to improve the ability of feature learning. Experimental results on five real PolSAR datasets demonstrate that the proposed method effectively captures edge details and preserves homogeneous regions for terrain classification, especially in heterogeneous regions.