Physics-Guided Regime Unmixing
About
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 $\xi_i \in [0,1]$ 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 $\rho > 0.90$.
Paula Pacheco, Pablo Granitto, Juan B. Cabral• 2026
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Hyperspectral Unmixing | Jasper Ridge | -- | 38 | |
| Hyperspectral Unmixing Reconstruction | Samson | SAD0.052 | 4 | |
| Hyperspectral Unmixing Reconstruction | Urban | SAD0.07 | 4 |
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