Data fusion for image classification using a Markov random field model

Christophe Lett; Josiane B. Zerubia

doi:10.1117/12.248559

26 August 1996 Data fusion for image classification using a Markov random field model

Christophe Lett, Josiane B. Zerubia

Proceedings Volume 2785, Vision Systems: New Image Processing Techniques; (1996) https://doi.org/10.1117/12.248559
Event: Lasers, Optics, and Vision for Productivity in Manufacturing I, 1996, Besancon, France

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.

Citation Download Citation

Christophe Lett and Josiane B. Zerubia "Data fusion for image classification using a Markov random field model", Proc. SPIE 2785, Vision Systems: New Image Processing Techniques, (26 August 1996); https://doi.org/10.1117/12.248559

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