Generalized linear mixing model accounting for endmember variability

TL;DR

This paper introduces a generalized linear mixing model (GLMM) for hyperspectral unmixing that better accounts for complex endmember spectral variability, improving accuracy over existing models.

Contribution

The paper proposes a new GLMM that models uneven spectral distortions and extends estimation methods to jointly determine variability and abundances.

Findings

GLMM outperforms existing models in synthetic and real data tests.

Increased flexibility improves unmixing accuracy.

Joint estimation enhances robustness to spectral variability.

Abstract

Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from illumination changes. In this paper, we further generalize the ELMM leading to a new model (GLMM) to account for more complex spectral distortions where different wavelength intervals can be affected unevenly. We also extend the existing methodology to jointly estimate the variability and the abundances for the GLMM. Simulations with real and synthetic data show that the unmixing process can benefit from the extra flexibility introduced by the GLMM.