Wavelet Principal Component Analysis and its Application to Hyperspectral Images

Abstract

We investigate reducing the dimensionality of image sets by using principal component analysis on wavelet coefficients to maximize edge energy in the reduced dimension images. Large image sets, such as those produced with hyperspectral imaging, are often projected into a lower dimensionality space for image processing tasks. Spatial information is important for certain classification and detection tasks, but popular dimensionality reduction techniques do not take spatial information into account. Dimensionality reduction using principal components analysis on wavelet coefficients is investigated. Equivalences and differences to conventional principal components analysis are shown, and an efficient workflow is given. Experiments on AVIRIS images show that the wavelet energy in any given subband of the reduced dimensionality images can be increased with this method.