26 August 1996 Data fusion for image classification using a Markov random field model
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christophe Lett, Christophe Lett, Josiane B. Zerubia, 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); doi: 10.1117/12.248559; https://doi.org/10.1117/12.248559
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