Resolution study of a Hyperspectral Sensor using Computed Tomography in the presence of Noise

A new type of hyperspectral imaging sensor is proposed, simulated and tested, which records both spectral and 2-dimensional spatial information. Dispersive imaging spectrometers typically measure multiple wavelengths and a single spatial dimension. Unlike dispersive imaging spectrometers, chromo-tomographic hyperspectral imaging sensors (CTHIS) record two spatial dimensions, as well as a spectral dimension, using computed tomography (CT) techniques with only a finite number of diverse images. CTHIS require a reconstruction algorithm in order to yield a usable hyperspectral data cube, and assume that the point spread function (PSF) is known. To date, the factors affecting resolution of these sensors have not been examined.;Lens-based CTHIS sensors use chromatic aberration of a lens and multiple images in varying levels of defocus to determine the chromatic scene of an object. This type of CTHIS sensor has many practical advantages including simplicity of its design and dual use as a broad band imager with no additional processing. The lens-based CTHIS concept has been largely unexplored up to this time. The results of this research effort serve to examine factors affecting the spectral and spatial resolution of a lens-based CTHIS sensor, specifically showing how many frames are needed to reconstruct the spectral cube of a simple object using a theoretical lower bound. In this research a new algorithm is derived and is used to successfully reconstruct a hyperspectral object in the presence of noise and background. This new algorithm is used to verify the number of frames predicted from the theoretical bound calculation using laboratory data, thereby demonstrating the validity of the bound calculation. Finally, a simple method is proposed and tested to use this sensor in the presence of atmospheric turbulence. This method is shown in simulation to successfully remove the effects of atmospheric turbulence and estimate the atmospheric seeing conditions blindly from raw lens-based CTHIS data.