A Physics-Guided Diffusion Unmixing Model for Global Lunar Mineral Abundance Mapping

Accurate global mapping of lunar regolith minerals is critical for understanding the geological evolution and resource potential of the Moon. However, spectral unmixing algorithms originally designed for terrestrial remote sensing often perform poorly when applied to the Moon, due to the nonlinear intimate mixing and spectral variability associated with its unique surface environment. To address these challenges, this study proposes a novel physics-guided radiative transfer model with a deep diffusion network, informed by spectral library prior, to estimate lunar mineral abundances from Chang'E-1 Interferometric Imaging Spectrometer (IIM) hyperspectral data. Single-scattering albedo (SSA) is retrieved by inverting the Hapke model, with photometric normalization to deal with multiple-scattering effects, observation geometry, and macroscopic roughness to mitigate extrinsic effects prior to unmixing. A physics-guided spectral library is constructed to capture intrinsic endmember variability arising from mineralogy and grain size. Spectral unmixing is then performed in the SSA domain using a diffusion model coupled with Bayesian abundance estimation. Validation against lunar sample data and 19 verified lunar sites confirms the accuracy of the proposed method. Global abundance maps for plagioclase, glass, orthopyroxene, olivine, ilmenite, and clinopyroxene exhibit improved spatial detail and better geological coherence compared to previous methods. These results validate the proposed method as a robust approach for global-scale lunar mineral mapping, with strong potential for integration into future planetary remote sensing missions.