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Proceedings Paper

Multispectral image classification using a mixture density model
Author(s): Sylvia S. Shen; Brian D. Horblit
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Paper Abstract

This paper describes a multispectral image classification technique. This technique involves two steps. First, we describe the underlying distribution of the pixel intensity vectors for the entire scene as a mixture of multivariate Gaussian distributions. We then use this mixture decomposition and a small number of labeled pixels to estimate the proportion of a mixture component that is comprised of a certain class, which enables us to use a Bayes-type decision rule to classify each pixel in the scene. Results of applying this technique to three-band SPOT data are presented. Comparisons with results obtained from a maximum likelihood classifier are also presented.

Paper Details

Date Published: 16 December 1992
PDF: 9 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130835
Show Author Affiliations
Sylvia S. Shen, Lockheed Palo Alto Research Lab. (United States)
Brian D. Horblit, Lockheed Palo Alto Research Lab. (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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