Alex Teterukovskiy and Jun Yu
Contextual Reclassification of Multispectral Images: A Markov Random Field Approach
This work presents methods for multispectral image classification using the
contextual classifiers based on Markov Random Field (MRF) models. Performance of
some conventional classification methods is evaluated, through a Monte Carlo
study, with or without using the contextual reclassification. Spatial
autocorrelation is present in the computer-generated data on a true scene. The
total misclassification rates for varying strengths of autocorrelation and for
different methods are compared. The results indicate that the combination of the
spectral-contextual classifiers can improve to a great extent the accuracy of
conventional non-contextual classification methods. It is also shown how the
most complicated cases can be handled by the Gibbs sampler.