Abstract
During the last few years, Markov Random Field (Mrf) models have already been successfully applied in some applications in image remote sensing in a context of conditional maximum likelihood estimation. Here, in the same context, we propose some original uses of Mrf, especially in image segmentation, noise filtering and discriminant analysis. For instance, we propose a Mrf model on the spectral signatures space, a strongly unified approach to classification and noise filtering as well as a particular model of noise.
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© 1993 Springer-Verlag Berlin Heidelberg
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Granville, V., Rasson, JP. (1993). Markov Random Field Models in Image Remote Sensing. In: Härdle, W., Simar, L. (eds) Computer Intensive Methods in Statistics. Statistics and Computing. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52468-4_7
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DOI: https://doi.org/10.1007/978-3-642-52468-4_7
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0677-9
Online ISBN: 978-3-642-52468-4
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