  This paper presents an unsupervised Bayesian algorithm for hyperspectral
image unmixing accounting for endmember variability. The pixels are modeled by
a linear combination of endmembers weighted by their corresponding abundances.
However, the endmembers are assumed random to take into account their
variability in the image. An additive noise is also considered in the proposed
model generalizing the normal compositional model. The proposed algorithm
exploits the whole image to provide spectral and spatial information. It
estimates both the mean and the covariance matrix of each endmember in the
image. This allows the behavior of each material to be analyzed and its
variability to be quantified in the scene. A spatial segmentation is also
obtained based on the estimated abundances. In order to estimate the parameters
associated with the proposed Bayesian model, we propose to use a Hamiltonian
Monte Carlo algorithm. The performance of the resulting unmixing strategy is
evaluated via simulations conducted on both synthetic and real data.
