Unsupervised hyperspectral band selection using parallel processing

Abstract

Band selection is a common technique to reducing the data dimensionality of hyperspectral imagery. When the desired object information is unknown, the objective of an unsupervised band selection approach is to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following data processing and analysis, the process itself may induce additional computation complexity. In this paper, we propose parallel processing techniques for an unsupervised band selection method without changing band selection result.