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Proceedings Paper

Algorithm for detecting anomaly in hyperspectral imagery using factor analysis
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Paper Abstract

Hyperspectral imaging is particular useful in remote sensing to identify a small number of unknown man-made objects in a large natural background. An algorithm for detecting such anomalies in hyperspectral imagery is developed in this article. The pixel from a data cube is modeled as the sum of a linear combination of unknown random variables from the clutter subspace and a residual. Maximum likelihood estimation is used to estimate the coecients of the linear combination and covariance matrix of the residual. The Mahalanobis distance of the residual is dened as the anomaly detector. Experimental results obtained using a hyperspectral data cube with wavelengths in the visible and near-infrared range are presented.

Paper Details

Date Published: 20 May 2011
PDF: 5 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804805 (20 May 2011); doi: 10.1117/12.886411
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)
John Ingram, U.S. Military Academy (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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