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Modality-Prior Aware Progressive Diffusion Pansharpening
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AI
DOI：10.1109/TGRS.2026.3663240.png)
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Abstract
En 中文
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 model 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 multilevel representations through content-aware dynamic learning, reconciling their inherent tradeoffs 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.
Keywords:
Diffusion model
multispectral
panchromatic (PAN)
pansharpening
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