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Proceedings Paper

Target-driven selection of lossy hyperspectral image compression ratios
Author(s): Jason R. Kaufman; Christopher D. McGuinness
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Paper Abstract

A common problem in applying lossy compression to a hyperspectral image is predicting its effect on spectral target detection performance. Recent work has shown that light amounts of lossy compression can remove noise in hyperspectral imagery that would otherwise bias a covariance-based spectral target detection algorithm’s background-normalized response to target samples. However, the detection performance of such an algorithm is a function of both the specific target of interest as well as the background, among other factors, and therefore sometimes lossy compression operating at a particular compression ratio (CR) will not negatively affect the detection of one target, while it will negatively affect the detection of another. To account for the variability in this behavior, we have developed a target-centric metric that guides the selection of a lossy compression algorithm’s CR without knowledge of whether or not the targets of interest are present in an image. Further, we show that this metric is correlated with the adaptive coherence estimator’s (ACE’s) signal to clutter ratio when targets are present in an image.

Paper Details

Date Published: 5 May 2017
PDF: 9 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 1019813 (5 May 2017); doi: 10.1117/12.2262920
Show Author Affiliations
Jason R. Kaufman, Univ. of Dayton Research Institute (United States)
Christopher D. McGuinness, Univ. of Dayton Research Institute (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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