A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.