Methods for Determining Best Multispectral Bands Using Hyperspectral Data

Over the years several methods have been used to determine the best bands for a visible near IR multi-spectral sensor. The most popular method, the committee method, places in one room scientists with differing opinions on the phenomena and the sensor mission, and a compromise set is developed. To avoid this, there have been several methods to automate this selection process. We have developed a method to examine hyperspectral data to find the best multispectral band set (whether 3, 4, 5 or 6 bands) based on the background, on the premise that, with the target unknown, the band set that best separates the background materials is the best. We start with a hyperspectral data set of an area representative of the potential area of operations for the candidate multispectral sensor. We then either select manually representative spectra at high spectral resolution or run a program for determining the spectral endmembers. The resulting hyperspectral endmembers are then input to an exhaustive search program. The goal of the exhaustive search is to find a set of N multispectral bands that maximizes the spectral angles between all of the endmembers. After examining often millions of combinations, the multispectral band set that maximizes the spectral separation is judged to be the best. We have applied this method to the selection of multispectral bands sets for several sensors.