Probabilistic Mixture Model-Based Spectral Unmixing

TL;DR

This paper introduces a probabilistic Bayesian mixture model for spectral unmixing that explicitly accounts for noise and uncertainty, enabling the extraction of endmembers from less diverse mixtures with reliable uncertainty estimates.

Contribution

It presents a novel probabilistic inference framework that unmixes spectral data while modeling noise and uncertainty, improving robustness in limited-diversity scenarios.

Findings

Effective endmember extraction with uncertainty quantification.

Robustness to non-isotropic Gaussian noise.

Applicable to mixtures with limited diversity.

Abstract

Identifying pure components in mixtures is a common yet challenging problem. The associated unmixing process requires the pure components, also known as endmembers, to be sufficiently spectrally distinct. Even with this requirement met, extracting the endmembers from a single mixture is impossible; an ensemble of mixtures with sufficient diversity is needed. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most of them assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference approach that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and presence of non-isotropic Gaussian noise. Access to reliable uncertainties on the unmixing solutions would enable robust solutions as well as informed decision making.