Scattering-Aware Diffusion Model: Enforcing Polarimetric Fidelity for PolSAR Data Generation

Polarimetric synthetic aperture radar (PolSAR) provides physically interpretable observations through polarization-dependent scattering responses. Developing reliable PolSAR interpretation methods is often constrained by data scarcity due to limited coverage of acquisition conditions and scene states. Generative models can mitigate this scarcity, but most treat Pol-SAR as generic multichannel imagery and neglect inter-channel polarimetric correlations that encode scattering characteristics. Consequently, generated samples may be visually plausible yet exhibit limited polarimetric fidelity, reducing their utility for PolSAR interpretation. To address this issue, we propose a Scattering Aware Diffusion Model (SADM) that enforces scattering consistency to improve polarimetric fidelity. Specifically, we devise a scattering-decoupling strategy within SADM that learns representations for each polarimetric channel independently to preserve physical meaning and captures scattering mechanisms via inter-channel correlations among these representations. This strategy then employs the resulting scattering descriptors to modulate the spatial features in the main generation pathway, endowing them with explicit scattering semantics. Furthermore, we incorporate a Scattering Mechanism Consistency loss to facilitate learning the joint distribution of PolSAR data and polarimetric features, enhancing polarimetric fidelity beyond pixel-level appearance. Experiments on a publicly available Ku-band full-polarimetric 10-class civilian vehicle dataset (KuPolVehicle-10) show that SADM achieves a Yamaguchi-based polarimetric Fréchet Inception Distance (Pol-FID) of 3.506 versus 5.245 for a standard Denoising Diffusion Probabilistic Model (DDPM) baseline. Moreover, SADM-based augmentation improves classification accuracy by 4.94%, indicating that enforcing polarimetric fidelity translates to improved utility in PolSAR interpretation. Additional evaluations on OpenSARShip 2.0 confirm that SADM generalizes well across polarization modes, frequency bands, and target classes.