Normal compositional models: generalizations and applications

David W. J. Stein

doi:10.1117/12.478753

2 August 2002 Normal compositional models: generalizations and applications

David W. J. Stein

Proceedings Volume 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII; (2002) https://doi.org/10.1117/12.478753
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

David W. J. Stein "Normal compositional models: generalizations and applications", Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); https://doi.org/10.1117/12.478753

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