Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework

TL;DR

This paper introduces a variational inference framework for hyperspectral unmixing that accounts for endmember variability and outliers, improving robustness and efficiency in complex spectral data analysis.

Contribution

It presents a novel VI-based approach with a patch-wise static endmember assumption, enabling lightweight optimization and handling of endmember variability and outliers.

Findings

Effective on synthetic, semi-real, and real datasets.

Outperforms existing methods in robustness and computational efficiency.

Utilizes priors like Beta for improved probabilistic modeling.

Abstract

This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. Following the marginalized maximum likelihood (MML) principle, a VI algorithmic structure is designed for probabilistic inference for HU-EV. Specifically, a patch-wise static endmember assumption is employed to exploit spatial smoothness and to try to overcome the ill-posed nature of the HU-EV problem. The design facilitates lightweight, continuous optimization-based updates under a variety of endmember priors. Some of the priors, such as the Beta prior, were previously used under computationally heavy, sampling-based probabilistic HU-EV methods. The effectiveness of the proposed framework is demonstrated through synthetic, semi-real, and real-data experiments.