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Proceedings Paper

Hyperspectral anomaly detection based on variable dimension for clutter subspace
Author(s): Edisanter Lo
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Paper Abstract

Anomaly detectors based on subspace models have the dimension of the clutter subspace as the parameter with a large range of values. An anomaly detector that has a different parameter with fewer values is proposed. The known pixel from a hyperspectral image is predicted with a linear transformation of the unknown variables from the clutter subspace and the coefficients of the linear transformation are unknown. The dimension of the clutter subspace can vary from one spectral component of the pixel to another. The anomaly detector is the Mahalanobis distance of the error. The experimental results show that the parameter in the anomaly detector has a significantly reduced number of possible values in comparison with the conventional anomaly detectors.

Paper Details

Date Published: 24 May 2012
PDF: 6 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839004 (24 May 2012); doi: 10.1117/12.920835
Show Author Affiliations
Edisanter Lo, Susquehanna Univ. (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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