Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD


Hyperspectral imagery providing both spatial and spectral information has diverse applications in remote sensing and scientific imaging scenarios. The development of the Chromotomographic Imaging System (CTIS) allows simultaneous collection of both spatial and spectral data by a two-dimensional (2D) focal plane detector array. Post-processing of the 2D detector data reconstructs the three-dimensional (3D) hyperspectral content of the imaged scene. This thesis develops Estimation Theory based algorithms for reconstructing the hyperspectral scene data. The initial algorithm developed reconstructs the 3D hyperspectral scene data cube. An additional algorithm reconstructs a matrix comprised of one spectral dimension and one compound spatial dimension. This spatial dimension consists of a vector sum along one spatial dimension of the 3D hyperspectral data cube. Methods for including the effects of atmospheric attenuation on the light over the propagation path are also included. The algorithms are evaluated using test cases consisting of blackbody point sources, monochromatic extended sources and blackbody extended sources. The results show good performance for reconstructing the absolute radiometry and spatial features of a hyperspectral scene data cube. These algorithms also do not significantly degrade in the presence of noisy detector data. The vector algorithm also exhibits stable performance behaviour when reconstructing a temporally evolving hyperspectral scene.

AFIT Designator


DTIC Accession Number