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Effect of manmade pixels on the inherent dimension of natural material distributionsFormat | Member Price | Non-Member Price |
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Paper Abstract
The inherent dimension of hyperspectral data may be a useful metric for discriminating between the presence of
manmade and natural materials in a scene without reliance on spectral signatures take from libraries. Previously,
a simple geometric method for approximating the inherent dimension was introduced along with results from
application to single material clusters. This method uses an estimate of the slope from a graph based on the
point density estimation in the spectral space. Other information can be gathered from the plot which may aid
in the discrimination between manmade and natural materials. In order to use these measures to differentiate
between the two material types, the effect of the inclusion of manmade pixels on the phenomenology of the
background distribution must be evaluated. Here, a procedure for injecting manmade pixels into a natural
region of a scene is discussed. The results of dimension estimation on natural scenes with varying amounts of
manmade pixels injected are presented here, indicating that these metrics can be sensitive to the presence of
manmade phenomenology in an image.
Paper Details
Date Published: 27 April 2009
PDF: 8 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341K (27 April 2009); doi: 10.1117/12.816568
Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 8 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341K (27 April 2009); doi: 10.1117/12.816568
Show Author Affiliations
Ariel Schlamm, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
William 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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