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Proceedings Paper

Anomaly clustering in hyperspectral images
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Paper Abstract

The topological anomaly detection algorithm (TAD) differs from other anomaly detection algorithms in that it uses a topological/graph-theoretic model for the image background instead of modeling the image with a Gaussian normal distribution. In the construction of the model, TAD produces a hard threshold separating anomalous pixels from background in the image. We build on this feature of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. This is done by identifying, and integrating, clusters of anomalous pixels via a graph theoretical method combining spatial and spectral information. The method is applied to a cluttered HyMap image and combines small groups of pixels containing like materials, such as those corresponding to rooftops and cars, into individual clusters. This improves visualization and interpretation of objects.

Paper Details

Date Published: 27 April 2009
PDF: 9 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341P (27 April 2009); doi: 10.1117/12.818407
Show Author Affiliations
Timothy J. Doster, Rochester Institute of Technology (United States)
David S. Ross, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
William F. Basener, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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