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.