Modality-Prior Aware Progressive Diffusion Pansharpening

Pansharpening entails significant challenges in fully exploiting the synergistic potential between generative expressiveness and discriminative precision, requiring advanced generative modeling guided by discriminative priors. To this end, a unified end-to-end, modality-prior aware progressive diffusion model (PPDM-Pan) is presented that dynamically integrates discriminative priors with generative representations through a progressive fusion process, achieving synergistic feature interaction and enhanced spatial–spectral consistency for high-fidelity high-resolution multispectral reconstruction. Specifically, to explicitly extract modality-specific discriminative priors, a modal prior guidance (MPG) encoder is designed to models complementary spatial and spectral priors from heterogeneous panchromatic (PAN) and low-resolution multispectral (LRMS) inputs, establishing robust modality-aware representations. Subsequently, a diffusion process is leveraged to model the complex, high-dimensional PAN-LRMS distribution and synthesize expressive latent features with enhanced generative capacity. Crucially, to orchestrate synergistic integration of generative features and discriminative priors, a progressive modality-aware fusion (PMAF) strategy is introduced that hierarchically refines multi-level representations through content-aware dynamic learning, reconciling their inherent trade-offs to deliver high-fidelity reconstruction with superior spatial-spectral consistency. Experiments on QuickBird, GaoFen-2, and WorldView-3 datasets demonstrate its superior performance in balancing spatial realism, characterized by visually realistic textures and structural details, and spectral fidelity, highlighting its effectiveness as a robust and versatile solution.