  When considering the problem of unmixing hyperspectral images, most of the
literature in the geoscience and image processing areas relies on the widely
used linear mixing model (LMM). However, the LMM may be not valid and other
nonlinear models need to be considered, for instance, when there are
multi-scattering effects or intimate interactions. Consequently, over the last
few years, several significant contributions have been proposed to overcome the
limitations inherent in the LMM. In this paper, we present an overview of
recent advances in nonlinear unmixing modeling.
