Share Email Print
cover

Proceedings Paper

Stochastic compositional models applied to subpixel analysis of hyperspectral imagery
Author(s): David W. J. Stein
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Hyperspectral data are often modeled using either a linear mixture or a statistical classification approach. The linear mixture model describes each spectral vector as a constrained linear combination of end-member spectra, whereas the classification approach models each spectra as a realization of a random vector having one of several normal distributions. In this work we describe a stochastic compositional model that synthesizes these two viewpoints and models each spectra as a constrained linear combination of random vectors. Maximum likelihood methods of estimating the parameters of the model, assuming normally distributed random vectors, are described, and anomaly and likelihood ratio detection statistics are defined. Detection algorithms derived from the classification, linear mixing, and stochastic compositional models are defined. Detection algorithms derived from the classification, linear mixing, and stochastic compositional models are compared using data consisting of ocean hyperspectral imagery to which the signature of a personal flotation device has been added at pixel fill fractions (PFF) of five and ten percent. These results show that detection algorithms based on the stochastic compositional model may significantly improve detection performance. For example, this study shows that, at a 5% PFF and a probability of detection of 0.8, the false alarm probabilities of anomaly and likelihood detection algorithms based on the stochastic compositional model are more than an order of magnitude lower than the false alarm probabilities of comparable algorithms based on either a linear unmixing algorithm or a Gaussian mixture model.

Paper Details

Date Published: 17 January 2002
PDF: 8 pages
Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); doi: 10.1117/12.453365
Show Author Affiliations
David W. J. Stein, Space and Naval Warfare Systems Ctr., San Diego (United States)


Published in SPIE Proceedings Vol. 4480:
Imaging Spectrometry VII
Michael R. Descour; Sylvia S. Shen, Editor(s)

© SPIE. Terms of Use
Back to Top