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Proceedings Paper

Enhancing the resolution of spectral images
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Paper Abstract

This research continues the development of the Model-Based Spectral Image Deconvolution (MBSID) algorithm first presented elsewhere. The deconvolution algorithm is based on statistical estimation and is used 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 photon arrival and 2) an in-depth knowledge of the spectral imaging sensor. With these two elements, the MBSID algorithm can, through image post-processing, increase the spectral resolution of the images. While MBSID algorithms can be developed for any spectral imaging system, this research focuses on an algorithm 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 spatially resolved 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. Before the algorithm is used on data collected with ASIS, it is proven with data collected using a set-up similar to that of ASIS. The lab data successfully shows that the MBSID algorithm can improve both the spatial and spectral resolution for a collected spectral image.

Paper Details

Date Published: 4 May 2006
PDF: 11 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623309 (4 May 2006); doi: 10.1117/12.664903
Show Author Affiliations
Travis F. Blake, Air Force Institute of Technology (United States)
Matthew E. Goda, Air Force Institute of Technology (United States)
Stephen C. Cain, Air Force Institute of Technology (United States)
Kenneth J. Jerkatis, Boeing LTS (United States)

Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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