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.

Links:

Citation:

S. Zou and A. Zare, “Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation.” Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017.

@InProceedings{sheng2017hyperspectral,
Title = {Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation},
Author = {Zou, Sheng and Zare, Alina},
Booktitle = {IEEE Intl. Conf. on Acoustics, Speech and Signal Processing}
Year = {2017},
Month = {Mar.},
Pages = {6200-6204},
}