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Proceedings Paper

Target detection in hyperspectral Imaging using logistic regression
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Paper Abstract

Target detection is an important application in hyperspectral imaging. Conventional algorithms for target detection assume that the pixels have a multivariate normal distribution. The pixels in most images do not have multivariate normal distributions. The logistic regression model, which does not require the assumption of multivariate normal distribution, is proposed in this paper as a target detection algorithm. Experimental results show that the logistic regression model can work well in target detection.

Paper Details

Date Published: 17 May 2016
PDF: 7 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400W (17 May 2016); doi: 10.1117/12.2223943
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)
Emmett Ientilucci, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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