Application of the wavelet packet subspace to reflective hyperspectral imagery

Abstract

Wavelet Packets have been used to detect trace gases in long-wave infrared hyperspectral imagery. Spectral features for gases in the long-wave infrared can be characterized as lorentzian emission and absorption features. This is unlike spectral features for materials in visible, near infrared, and short-wave infrared, which are dependent on both the source illumination and the physical reflective properties of the surface material. In the reflective domain, features are represented by a much greater variety of shapes and distributions. These types of features are ideal for an adaptive target signature approach such as the Wavelet Packet Subspace (WPS). The WPS technique applies the wavelet packet transform and selects a best basis for pattern matching. The wavelet packet transform is an extension of the wavelet transform, which fully decomposes a signal into a library of wavelet packet bases. An orthogonal best basis is chosen which best represents features in the target signature at multiple resolutions. This best basis is then used for target detection. In this research, the Wavelet Packet Subspace technique is extended to reflective hyperspectral imagery. Using hyperspectral imagery data with known ground truth, a quantitative comparison is made between the WPS technique and other spectral matching methods. Spectral angle mapper, and clutter matched filter, and WPS are compared. Initial results demonstrate that performance of the WPS technique for reflective hyperspectral imagery is comparable to that of existing methods.