Physics-Informed Blind Hyperspectral Unmixing of Altered Minerals via Backscattering Parameter Learning

The blind hyperspectral unmixing of altered minerals holds significant indicative importance for the exploration of sandstone-type uranium deposits. Nevertheless, the distinctive backscattering and multiphase characteristics are typically underestimated by existing methods, resulting in limited accuracy in the unmixing performance of complex mixtures. This study presents a physics-informed Swin Transformer Network (PIST−Net) for the blind hyperspectral unmixing of hematite, goethite, biotite, and chlorite. The proposed model includes a lightweight Swin Transformer encoder for the long-range modeling of spatial and spectral features. Furthermore, a dual-branch decoder with adaptive physical parameters was introduced in the spectral reconstruction section. In this decoder, the backscattering prediction head independently estimates the backscattering factor for each endmember, accounting for the true behavior of the mineral materials. As a key parameter of the Hapke model, the learnable strategy can reduce the spectral error in the single-scattering albedo (SSA) space to improve the accuracy of abundance estimation. The results show that PIST−Net consistently outperformed all six competing models on the altered mineral (AM) dataset and the NASA Reflectance Experiment Laboratory (RELAB) dataset. For mixtures composed of 2–4 endmembers, the mean RMSE of estimated abundances ranges from 0.0312 to 0.1037.