Algorithms and Prototyping of a Compressive Hyperspectral Imager

Compressive sensing (CS) has recently emerged as an efficient technique for sampling a signal with fewer coefficients than dictated by classical Shannon/Nyquist theory. The assumption underlying this approach is that the signal to be sampled must have a concise representation in a convenient basis. In CS, sampling is performed by taking a number of linear projections of the signal onto pseudorandom sequences, while reconstruction exploits knowledge of a domain where the signal is "sparse". CS has also been used to develop innovative "compressive" imaging systems. CS could be used to design cheaper sensors, or sensors providing better resolution for an equal number of detectors. While compressive hyperspectral imaging has been studied in simulation, there are very few practical implementations. In this chapter we describe a prototype implementation of a compressive hyperspectral imager, highlighting design and data quality issues.