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23 August 2005 Spectral image deconvolution using sensor models
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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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Travis F. Blake, Stephen C. Cain, Matthew E. Goda, and Kenneth J. Jerkatis "Spectral image deconvolution using sensor models", Proc. SPIE 5896, Unconventional Imaging, 589606 (23 August 2005); https://doi.org/10.1117/12.613645
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