A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation

TL;DR

This paper introduces a spatial compositional model (SCM) for hyperspectral unmixing that incorporates spatial priors to improve endmember and abundance estimation, providing more accurate results and uncertainty quantification.

Contribution

The paper presents a novel SCM that integrates spatial information into the normal compositional model for enhanced unmixing and uncertainty estimation.

Findings

SCM outperforms state-of-the-art algorithms in accuracy.

SCM provides reliable uncertainty estimates.

Incorporating spatial priors improves unmixing results.

Abstract

The normal compositional model (NCM) has been extensively used in hyperspectral unmixing. However, most of the previous research has focused on estimation of endmembers and/or their variability. Also, little work has employed spatial information in NCM. In this paper, we show that NCM can be used for calculating the uncertainty of the estimated endmembers with spatial priors incorporated for better unmixing. This results in a spatial compositional model (SCM) which features (i) spatial priors that force neighboring abundances to be similar based on their pixel similarity and (ii) a posterior that is obtained from a likelihood model which does not assume pixel independence. The resulting algorithm turns out to be easy to implement and efficient to run. We compared SCM with current state-of-the-art algorithms on synthetic and real images. The results show that SCM can in the main provide more accurate endmembers and abundances. Moreover, the estimated uncertainty can serve as a prediction of endmember error under certain conditions.