A Hapke Physics-Guided Deep Autoencoder for Lunar Hyperspectral Unmixing

Accurate mapping of lunar mineral distributions is essential for understanding the Moon’s origin and evolution and for enabling future in situ resource utilization (ISRU). Yet mineralogical inversion from orbital hyperspectral observations remains challenging due to limited spatial resolution, complex photometric conditions, and sparse returned samples. We present PGU-Net, a Hapke physics-guided deep autoencoder for nonlinear blind unmixing of lunar hyperspectral data. The encoder adopts a dual-attention design to enhance discriminative spectral features. The decoder performs linear mixing in the SSA domain and then reconstructs reflectance through a lightweight nonlinear module, while physics-consistent losses encourage radiative-transfer plausibility. Experiments on a synthetic lunar regolith dataset demonstrate that PGU-Net achieves consistently lower endmember SAD and abundance aRMSE than representative baselines across multiple noise levels. Additional validations on the terrestrial AVIRIS Cuprite benchmark and on Moon Mineralogy Mapper (M3) observations near the Chang’e-5 (CE-5) and Chang’e-6 (CE-6) landing regions yield physically plausible mineral distributions. The M3 maps are broadly consistent with Kaguya MI mineral products and returned-sample constraints, supporting the practicality of PGU-Net for lunar mineralogical mapping.