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Proceedings Paper

Data fusion for image classification using a Markov random field model
Author(s): Christophe Lett; Josiane B. Zerubia
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Paper Abstract

In this paper, we present a method of classifying multi- channel images using a Markov random field monogrid or multiscale model. If the parameters of the model are known, the classification is called supervised and a training is necessary. If not, it is called unsupervised and requires an automatic parameter estimation. Then we compute an energy function of the system that we minimize using either deterministic relaxation techniques or stochastic methods, which gives us the classification. The multi-channel data take the form of multi-band aerial or satellite images as well as synthetic images.

Paper Details

Date Published: 26 August 1996
PDF: 8 pages
Proc. SPIE 2785, Vision Systems: New Image Processing Techniques, (26 August 1996); doi: 10.1117/12.248559
Show Author Affiliations
Christophe Lett, INRIA (France)
Josiane B. Zerubia, INRIA (France)


Published in SPIE Proceedings Vol. 2785:
Vision Systems: New Image Processing Techniques
Philippe Refregier, Editor(s)

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