  This paper presents a semi-supervised hyperspectral unmixing solution that
integrate the spatial information in the abundance estimation procedure. The
proposed method is applied on a nonlinear model based on polynomial
postnonlinear mixing model where characterizes each pixel reflections composed
of nonlinear function of pure spectral signatures added by noise. We
partitioned the image to classes where contains similar materials so share the
same abundance vector. The spatial correlation between pixels belonging to each
class is modelled by Markov Random Field. A Bayesian framework is proposed to
estimate the classes and corresponding abundance vectors alternatively. We
proposed sparse Dirichlet prior for abundance vector that made it possible to
use this algorithm in semi-supervised scenario where the exact involved
materials are unknown. In this approach, we just need to have a large library
of pure spectral signatures including the desired materials. An MCMC algorithm
is used to estimate the abundance vector based on generated samples. The result
of implementation on simulated data shows the prominence of proposed approach.
