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Proceedings Paper

Normal compositional models: generalizations and applications
Author(s): David W. J. Stein
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Paper Abstract

The normal compositional model (NCM) is a descriptive model that explicitly accounts for sub-pixel mixing and random variation of the spectrum of a material. In this paper the normal compositional model, defined in an earlier work, is extended to include an additive term that may represent path radiance and additive sensor noise. If the covariance matrix of the additive term is non-singular, as may be assumed since it includes the covariance matrix of the additive noise, the covariance matrix of the other classes need not be non-singular. Thus the current model synthesizes the linear unmixing and Gaussian clustering algorithms. Anomaly and matched target detection algorithms based on these three models are compared using ocean hyperspectral imagery, and for these data the NCM approach reduces the false alarm probability by more than an order of magnitude. The linear mixture and normal compositional models separate surface reflections and upwelling light more effectively than the Gaussian clustering algorithm. Furthermore, greater inter-band correlation is estimated using the subpixel covariance estimation methodology than using the pure pixel modeling approach.

Paper Details

Date Published: 2 August 2002
PDF: 9 pages
Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); doi: 10.1117/12.478753
Show Author Affiliations
David W. J. Stein, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 4725:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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