Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
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.
Cite
@article{arxiv.1609.03500,
title = {Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation},
author = {Sheng Zou and Alina Zare},
journal= {arXiv preprint arXiv:1609.03500},
year = {2016}
}