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Transformation for target detection in hyperspectral imaging
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Paper Abstract

Conventional algorithms for target detection in hyperspectral imaging usually require multivariate normal distributions for the background and target pixels. Significant deviation from the assumed distributions could lead to incorrect detection. It is possible to make the non-normal pixels into more normal-looking pixels by using a transformation on the pixels. A multivariate transformation based maximum likelihood is proposed in this paper to improve target detection in hyperspectral imaging. Experimental results show that the distribution of the transformed pixels become closer to a multivariate normal distribution and the performance of the detection algorithms improves after the transformation.

Paper Details

Date Published: 5 May 2017
PDF: 8 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980Z (5 May 2017); doi: 10.1117/12.2263887
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)
Emmett Ientilucci, Rochester Institute of Technology (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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