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Proceedings Article

Hyperspectral anomaly detection based on minimum generalized variance method

[+] Author Affiliations
Edisanter Lo

Susquehanna Univ.

John Ingram

U.S. Military Academy

Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 696603 (April 11, 2008); doi:10.1117/12.778929
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From Conference Volume 6966

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, FL | March 16, 2008

abstract

Anomaly detection for hyperspectral imaging is typically based on the Mahalanobis distance. The sample statistics for Mahalanobis distance are not resistant to the anomalies that are present in the sample pixels. Consequently, the sample statistics do not estimate the corresponding population parameters accurately. In this paper, we will present an algorithm for hyperspectral anomaly detection based on the Mahalanobis distance computed using robust statistics which are estimated based on the minimum generalized variance of the sample pixels. Numerical results based on actual hyperspectral images will be presented.

© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Edisanter Lo and John Ingram
"Hyperspectral anomaly detection based on minimum generalized variance method", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 696603 (April 11, 2008); doi:10.1117/12.778929; http://dx.doi.org/10.1117/12.778929


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