Information theory-based band selection for multispectral systems

This paper describes a methodology we have developed for wavelength band selection. This methodology combines an information theory-based criterion for selection with a genetic algorithm for searching for a near-optimal solution. We have applied this methodology to 302 material spectra in the Nonconventional Exploitation Factors (NEF) database to determine the band locations for 7, 15, 30, and 60-band sets that permit the best material separation. These optimal band sets were also evaluated in terms of their utility related to anomaly/target detection using multiband images generated from a Hyperspectral Digital imagery Collection Experiment (HYDICE) image cube. The optimal band locations and their corresponding entropies are given in this paper. Also presented are the anomaly/target detection results obtained from using these optimal band sets.