We performed a series of image classification experiments incorporating three independent band selection methodologies (derivative magnitude, fixed interval and derivative histogram), in order to explore the effects of spectral resampling on classification resiliency. This research verified that a minimum of seven, strategically located bands in the VIS–NIR wavelength region (425.4 nm, 514.9 nm, 560.1 nm, 685.5 nm, 731.5 nm, 812.3 nm and 916.7 nm) are necessary to maintain a classification resiliency above the 85 % threshold. Significantly, these seven bands produced the highest classification resiliency using the fewest number of bands of any of the 63 band-reduction strategies that were tested.
Analyzing only derivative magnitudes proved to be an unreliable tool to identify optimal bands. The fixed interval method was adversely influenced by the starting band location, making its implementation problematic. The combined use of derivative magnitude and frequency of occurrence appears to be the best method to determine the “optimal” bands for a wetland mapping hyperspectral application.