We first classified the image acquired in 2006 by using the maximum likelihood classifier (Richards and Jia 1999) and five training area sets corresponding to: forest, pasture, bare soil, urban area, and cultivated land cover classes.
The supervised classification technique by maximum likelihood classifier has been employed to create a classified image and has been assessed based on Kappa index.
The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM).
The overall and individual classification results of this approach were compared to the maximum likelihood classifier and the artificial neural network techniques.
Accordingly, supervised classification was done using training areas obtained from ground truth data combined with spectral signatures in false color composite images by maximum likelihood classifier.
The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm.
Traditionally the maximum likelihood classifier is used to obtain thematic maps in land use.
Early studies of land-cover classification used statistical methods such as the maximum likelihood classifier.
The maximum likelihood classifier applies a number of geometric models to minimize within group variability.
The maximum likelihood classifier identifies an optimal set of groups using a model of ellipsoids with equal volume and equal shape to define cluster membership.
