Improving classical contextual classifications
This paper shows some combinations of classifiers that achieve high accuracy classifications. Traditionally the maximum likelihood classification is used as an initial classification for a contextual classifier. We show that by using different non-parametric spectral classifiers to obtain the initial classification, we can significatively improve the accuracy of the classification with a reasonable computational cost. In this work we propose the use of different spectral classifications as initial maps for a contextual classifier (ICM) in order to obtain some interesting combinations of spectral-contextual classifiers for remote sensing image classification with an acceptable trade-off between the accuracy of the final classification and the computational effort required.
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