Physics-Guided Regime Unmixing
For hyperspectral unmixing, this provides a physically interpretable way to handle multiple scattering, outperforming fixed-regime nonlinear models.
PGRU estimates a pixel-wise scalar to activate nonlinear mixing only where justified, improving spectral unmixing on three datasets with physical coherence >0.90.
The Linear Mixing Model (LMM) dominates spectral unmixing for its simplicity, but fails under multiple scattering; existing nonlinear models compensate by applying a fixed regime uniformly across entire scenes. We propose Physics-Guided Regime Unmixing (PGRU), which estimates a pixel-wise scalar $ξ_i \in [0,1]$ from observable physical features to activate nonlinear mixing only where justified. Residuals from the Generalized Bilinear Model (GBM), the Post-Nonlinear Mixing Model (PPNM), and Hapke are combined via learned attention, yielding interpretable regime maps. Experiments on Samson, Jasper Ridge, and Urban show consistent improvements over baselines, with physical coherence $ρ> 0.90$.