Physics-Guided Regime Unmixing

TL;DR

The paper introduces Physics-Guided Regime Unmixing (PGRU), a method that adaptively applies nonlinear spectral unmixing models at the pixel level based on physical features, improving accuracy and interpretability.

Contribution

PGRU estimates pixel-wise nonlinear activation scalars using physical features, enabling adaptive and interpretable spectral unmixing across scenes.

Findings

PGRU outperforms fixed-regime models on multiple datasets.

Physical coherence of regime maps exceeds 0.90.

Adaptive unmixing improves spectral analysis accuracy.

Abstract

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 $\rho > 0.90$.