Fast Band Selection for Hyperspectral Imagery

Abstract

Band selection is a common technique for dimensionality reduction of hyperspectral imagery. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. However, it may be time-consuming for unsupervised band selection methods that need to take all pixels into consideration. Here, we propose an approach to select several pixels for unsupervised band selection and the number of pixels required can be equal to the number of bands to be selected minus 1. With whitened pixel signatures (not the original pixels), band selection performance can be comparable to or even better than that from using all the pixels. For this approach, graphics processing unit (GPU)-based parallel computing is implemented for pixel selection only to further expedite the process, since computational complexity in band selection has been greatly reduced.