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Evaluation of conventional digital camera scenes for Thematic Information Extraction
 Figure 5: The classified image obtained using Maximum Likelihood classifier for Merbok River estuary (Green = Forest, Blue = Water, Orange = Land, and Red =Urban) (C) Area C The Kappa coefficient and overall accuracy value for the three-classification technique are shown in Table 7. The others accuracy assessment results are presented in Tables 8 and 9, where the Kappa coefficient, the confusion matrix and the accuracy of each class using Maximum Likelihood, minimum distance-to-mean and parallelepiped classification are given, respectively. From the present analysis, one can see that the Maximum Likelihood classifier produced the best image classification accuracy with the highest overall accuracy and Kappa coefficient. The overall classification accuracies achieved by the proposed Maximum Likelihood classifier on the digital image is 79.50 %. This followed by the Minimum Distance-to-Mean with the overall classification accuracy of 76.50%, and Parallelepiped resulted in the overall classification accuracy of 10.00%. A classified image using Maximum Likelihood classifier is shown in Figure 6.  Figure 6: The classified image obtained using Maximum Likelihood classifier for Merbok River estuary (Green = Forest, Blue = Water, Orange = Land, and red = Urban)
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