Hyperspectral imaging sensors capture digital images in hundreds of contiguous spectral bands, allowing remote material identification. Most algorithms for identifying materials characterize the materials according to spectral
information only, ignoring potentially valuable spatial relationships. This paper investigates the use of integrated spatial
and spectral information for characterizing materials. It examines the specific situation where a set of pixels has
resolution such that it contains spatial patterns of mixed pixels. An autoregressive Gauss-Markov random field (GMRF)
is used to model the predictability of a target pixel from neighboring pixels. At the resolution of interest, the GMRF
model can successfully classify spatial patterns of aircraft and a residential area from the HYDICE airborne sensor
Desert Radiance field collection at Davis Monthan Air Force Base, Arizona.