Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
This addresses spectral unmixing in hyperspectral imaging for remote sensing applications, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled hyperspectral unmixing with endmember variability by applying Partial Membership Latent Dirichlet Allocation (PM-LDA), which models endmembers as Normal distributions and proportions with a Dirichlet distribution, and results on real data showed that PM-LDA produced endmember distributions representing ground truth classes and their variability.
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.