Abstract:
This dissertation describes research into a new remote sensing method to detect
trace gases in hyperspectral and ultra-spectral data. This new method is based on the
wavelet packet transform. It attempts to improve both the computational tractability and
the detection of trace gases in airborne and spaceborne spectral imagery. Atmospheric
trace gas research supports various Earth science disciplines to include climatology,
vulcanology, pollution monitoring, natural disasters, and intelligence and military
applications. Hyperspectral and ultra-spectral data significantly increases the data glut of
existing Earth science data sets. Spaceborne spectral data in particular significantly
increases spectral resolution while performing daily global collections of the earth.
Application of the wavelet packet transform to the spectral space of hyperspectral and
ultra-spectral imagery data potentially improves remote sensing detection algorithms. It
also facilities the parallelization of these methods for high performance computing. This
research seeks two science goals, 1) developing a new spectral imagery detection
algorithm, and 2) facilitating the parallelization of trace gas detection in spectral imagery
data.
