Hyperspectral Imagery is characterized by its wealth of spectral information, which makes it ideal for spectral classification. High spectral resolution comes at the cost of spatial resolution, however, making spatial classification difficult. As part of a thrust to develop a more optimal approach that uses both spatial and spectral information, we examine how high spectral resolution can be used to enhance spatial pattern recognition. We focus on targets made up of less than about five pixels, and thus have little shape or orientation information in individual bands. We then use an "Adaptive Spectral Unmixing" (ASU) operator on the hyperspectral data to estimate sub-pixel abundances as accurately as possible. Noting that vehicles of interest are often symmetric shapes, we demonstrate that geometric moments can be useful tools for rotationally-invariant shape discrimination of small targets. We use a pattern-matching strategy for spatial pattern recognition, and use the moments to guide our search of potential target templates. This approach avoids the under-constrained problem of trying to distill source shape characteristics, in all of their possible variations, from the abundance space. We describe the software testing package used, and present the results of preliminary tests on hyperspectral data.