Semisupervised hyperspectral image unmixing using a variational Bayes algorithm

TL;DR

This paper introduces a variational Bayes algorithm for semisupervised hyperspectral image unmixing, utilizing a Laplace prior to enforce sparsity and nonnegativity, with demonstrated effectiveness on real data.

Contribution

The paper proposes a novel variational Bayes approach with a Laplace prior for hyperspectral unmixing, combining sparsity and nonnegativity constraints.

Findings

Effective unmixing on Aviris Cuprite dataset

Outperforms existing methods in accuracy

Demonstrates robustness to noise

Abstract

This technical report presents a variational Bayes algorithm for semisupervised hyperspectral image unmixing. The presented Bayesian model employs a heavy tailed, nonnegatively truncated Laplace prior over the abundance coefficients. This prior imposes both the sparsity assumption and the nonnegativity constraint on the abundance coefficients. Experimental results conducted on the Aviris Cuprite data set are presented that demonstrate the effectiveness of the proposed method.