  The normal compositional model (NCM) has been extensively used in
hyperspectral unmixing. However, most of the previous research has focused on
estimation of endmembers and/or their variability. Also, little work has
employed spatial information in NCM. In this paper, we show that NCM can be
used for calculating the uncertainty of the estimated endmembers with spatial
priors incorporated for better unmixing. This results in a spatial
compositional model (SCM) which features (i) spatial priors that force
neighboring abundances to be similar based on their pixel similarity and (ii) a
posterior that is obtained from a likelihood model which does not assume pixel
independence. The resulting algorithm turns out to be easy to implement and
efficient to run. We compared SCM with current state-of-the-art algorithms on
synthetic and real images. The results show that SCM can in the main provide
more accurate endmembers and abundances. Moreover, the estimated uncertainty
can serve as a prediction of endmember error under certain conditions.
