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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 46, Iss. 19 — Jul. 1, 2007
  • pp: 4162–4163

Comments on “Comparison between orthogonal subspace projection and background subtraction techniques applied to remote-sensing data”

Steven Johnson  »View Author Affiliations

Applied Optics, Vol. 46, Issue 19, pp. 4162-4163 (2007)

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Ben-David and Ren [Appl. Opt. 44, 3846 (2005)] discussed methods of estimating the concentration of chemical vapor plumes in hyperspectral images. The authors of that paper concluded that a technique called orthogonal subspace projection (OSP) produces better concentration estimates than background subtraction when certain stochastic noise conditions are present in the data. While that conclusion is correct, it is worth noting that the data can be whitened to improve the performance of the background subtraction method. In particular, if the noise is multivariate Gaussian, then whitening will ensure that the background subtraction method is superior to OSP.

© 2007 Optical Society of America

OCIS Codes
(000.5490) General : Probability theory, stochastic processes, and statistics
(070.4790) Fourier optics and signal processing : Spectrum analysis

ToC Category:
Probability, Theory, Stochastic Processes, and Statistics

Original Manuscript: October 3, 2005
Revised Manuscript: January 2, 2007
Manuscript Accepted: January 23, 2007
Published: June 12, 2007

Steven Johnson, "Comments on "Comparison between orthogonal subspace projection and background subtraction techniques applied to remote-sensing data"," Appl. Opt. 46, 4162-4163 (2007)

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