A Bayesian model and Gibbs sampler for hyperspectral imaging

In this ongoing work, we propose a Bayesian model that can be used to detect targets in multispectral images when the signals from the materials in the image mix linearly, the noise is Gaussian, and abundance parameters are nonnegative. By using efficient implementations of the Gibbs sampler, the expectation of any measurable functional of the abundance parameters, relative to the posterior distribution, can be computed easily. This general approach can be used to include additional constraints.