A method that facilitates identification of features in a scene enables
enhanced detail to be displayed. One embodiment incorporates a multi-grid
Gibbs-based algorithm to partition sets of endmembers of an image into
smaller sets upon which spatial consistency is imposed. At each site
within an imaged scene, not necessarily a site entirely within one of the
small sets, the parameters of a linear mixture model are estimated based
on the small set of endmembers in the partition associated with that
site. An, enhanced spectral mixing process (SMP) is then computed. One
embodiment employs a simulated annealing method of partitioning
hyperspectral imagery, initialized by a supervised classification method
to provide spatially smooth class labeling for terrain mapping
applications. One estimate of the model is a Gibbs distribution defined
over a symmetric spatial neighborhood system that is based on an energy
function characterizing spectral disparities in both Euclidean distance
and spectral angle.