Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation

TL;DR

This paper introduces a novel hyperspectral unmixing method using Partial Membership Latent Dirichlet Allocation (PM-LDA) that models spectral variability and spatial information to improve endmember estimation.

Contribution

The paper applies PM-LDA with Normal Compositional Model to hyperspectral unmixing, effectively capturing spectral variability and spatial context in a unified probabilistic framework.

Findings

PM-LDA accurately estimates endmember distributions.

The method accounts for spectral variability effectively.

Results align well with ground truth classes.

Abstract

The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use of superpixel-based 'documents.' In our application of PM-LDA, we employ the Normal Compositional Model in which endmembers are represented as Normal distributions to account for spectral variability and proportion vectors are modeled as random variables governed by a Dirichlet distribution. The use of the Dirichlet distribution enforces positivity and sum-to-one constraints on the proportion values. Algorithm results on real hyperspectral data indicate that PM-LDA produces endmember distributions that represent the ground truth classes and their associated variability.