Abstract
This research develops a Model-based Spectral Image Deconvolution
(MBSID) algorithm based on statistical estimation to spectrally
deconvolve images collected from a spectral imaging sensor. The
development of the algorithm requires only two key elements, 1) the
statistics of the light arrival and 2) an in-depth knowledge of the
spectral imaging sensor. With these two elements, the MBSID
algorithm can, through image post-processing, dramatically increase
the spectral resolution of the images as well as give insight into
the performance of the imaging sensor itself. While MBSID algorithms
can be developed for any spectral imaging system, for this research
an algorithm is developed for ASIS (AEOS Spectral Imaging Sensor), a
new spectral imaging sensor installed with the 3.6m Advanced
Electro-Optical System (AEOS) telescope at the Maui Space
Surveillance Complex (MSSC). The primary purpose of ASIS is to take
spatial and spectral images of space objects. The stringent
requirements associated with imaging these objects, especially the
low-light levels and object motion, required a sensor design with
less spectral resolution than required for image analysis. However,
by applying MBSID to the collected data, the sensor will be capable
of achieving a much higher spectral resolution, allowing for better
spectral analysis of the space object.
