We report the results of a tradeoff study for the selection of the number of wavelength bands and resolution needed in a hyperspectral data set in order to separate a scene into its constituent features. This separation is accomplished by finding approximate endmembers using convex mixing and shrink-wrapping techniques. This and related techniques are referred to as NRL's Optical Real-time Adaptive Spectral Identification System (ORASIS). ORASIS's algorithms will be briefly described. Once endmembers are found, matched filters are calculated which can then be used to separate (or demix) the scene. We have analyzed synthetic cubes, cubes acquired by NRL's Portable Hyperspectral Imager for Low Light Spectroscopy (PHILLS) sensor, and cubes from other sensors. PHILLS consists of multiple hyperspectral sensors that operate in pushbroom mode. PHILLS has been deployed from aircraft and on the ground in a variety of terrains from the polar icecap to the Florida Keys. The majority of the data were recorded with a 16-bit thermo-electrically cooled camera which records 1024 wavelengths over the range of 200 to 1100 nm. Major features of the scene can be successfully demixed using fewer than 1024 wavelength bands. However, preliminary evidence suggests that finer features require the full wavelength range and resolution.