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Space object detection: receiver operating characteristics for Poisson and normally distributed data
Author(s): Richard McMurry
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Paper Abstract

Object detection algorithms typically implement a Likelihood Ratio Test (LRT) for determining if an image-pixel contains a star. Implementing a LRT requires prior knowledge about the statistics of the data, such as the mean and variance. Assuming the background noise follows a Gaussian distribution, the mean and the variance have to be calculated separately. If the background data follows a Poisson distribution, the mean is the only calculation needed, as mean and variance of the Poisson distribution equal each other. This paper will compare the possible detection improvements when using a Poisson assumption. Many star detection LRTs will use a windowing technique to limit the amount of background data that is being tested. Various window sizes will also be tested to determine possible detection improvements that can be realized.

Paper Details

Date Published: 20 September 2016
PDF: 6 pages
Proc. SPIE 9982, Unconventional Imaging and Wavefront Sensing XII, 99820X (20 September 2016); doi: 10.1117/12.2237833
Show Author Affiliations
Richard McMurry, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9982:
Unconventional Imaging and Wavefront Sensing XII
Jean J. Dolne; Thomas J. Karr; David C. Dayton, Editor(s)

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