Physics-Guided Regime Unmixing

Fuente: arXiv
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Main Authors: Pacheco, Paula, Granitto, Pablo, Cabral, Juan B.
Format: Preprint
Published: 2026
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author Pacheco, Paula
Granitto, Pablo
Cabral, Juan B.
author_facet Pacheco, Paula
Granitto, Pablo
Cabral, Juan B.
contents 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 $ρ> 0.90$.
format Preprint
id arxiv_https___arxiv_org_abs_2605_04247
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Physics-Guided Regime Unmixing
Pacheco, Paula
Granitto, Pablo
Cabral, Juan B.
Computer Vision and Pattern Recognition
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 $ρ> 0.90$.
title Physics-Guided Regime Unmixing
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2605.04247