A spectral imager provides a 3-D data cube in which the spatial information (2-D) of the image is complemented
by spectral information (1-D) about each spatial location. Typically, these systems are operated in a
fully-determined (or overdetermined) manner so that the measurements can be computationally inverted into a
reliable estimate of the source. We propose a notional system design that is highly underdetermined, yet still
computationally invertable. This approach relies on recently-developed concepts in compressive sensing. Because
the number of required measurements is greatly reduced from traditional designs, the result is a faster and more
economical sensor system.