Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation

TL;DR

This paper introduces a semi-supervised approach using Partial Membership Latent Dirichlet Allocation for hyperspectral unmixing, effectively handling spectral variability and spatial data to improve endmember estimation.

Contribution

It extends PM-LDA to incorporate imprecise label information, enhancing unmixing accuracy and endmember estimation in hyperspectral data.

Findings

Improved unmixing results on two datasets

Effective handling of spectral variability

Enhanced endmember estimation accuracy

Abstract

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging spatial information. In this work, we extend Partial Membership Latent Dirichlet Allocation to incorporate any available (imprecise) label information to help guide unmixing. Experimental results on two hyperspectral datasets show that the proposed semi-supervised PM-LDA can yield improved hyperspectral unmixing and endmember estimation results.