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Proceedings Article

A hyperspectral anomaly detector based on partialing out a clutter subspace

[+] Author Affiliations
Edisanter Lo

Susquehanna Univ. (USA)

Alan Schaum

Naval Research Lab. (USA)

Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733404 (April 27, 2009); doi:10.1117/12.821012
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From Conference Volume 7334

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, Florida, USA | April 13, 2009

abstract

An anomaly detector for hyperspectral imaging based on partialling out the effect of the clutter subspace is devised. The partialling maximizes the squared correlation between each spectral component and a linear predictor, with no restrictions on the form of the probability distribution. The detection step is defined by thresholding a Mahalanobis measure of the prediction error. The method is compared to conventional anomaly detectors using VNIR hyperspectral imagery.

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Edisanter Lo and Alan Schaum
"A hyperspectral anomaly detector based on partialing out a clutter subspace", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733404 (April 27, 2009); doi:10.1117/12.821012; http://dx.doi.org/10.1117/12.821012


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