Log-linear modelling for the evaluation of the variables affecting the accuracy of probabilistic, fuzzy and neural network classifications

The accuracy of an image classification is a function of a range of variables. To select an appropriate classification approach for a particular set of data the analyst must be aware of the significant variables which may affect classification accuracy and the nature of their effect. The effect of a variable, or small number of variables, on classification accuracy may be evaluated by straightforward comparison of classification accuracies. However, for the evaluation of the simultaneous effect of a large number of variables such an approach may be impractical. In such circumstances log-linear modelling may be used to identify the significant variables affecting classification accuracy and the nature of the effect of the significant variables elucidated from further analysis. Log-linear modelling was used here to evaluate the effect of four variables (training set size, waveband combination, classification algorithm and testing set size) on classification accuracy. Since the analyst usually has most control over the choice of classification algorithm most attention was focused on the effects of the other three variables on the accuracies of classifications derived from conventional probabilistic, fuzzy set and neural network classification algorithms. The results showed that these classification algorithms were sensitive to variations in the other three variables. Overall the neural network classifications were generally the most accurate. The accuracies of the neural network classifications were, however, most influenced by training set size, with higher accuracies obtained with the use of large training sets. Alternatively, the other classification algorithms were least affected by the training set size and more sensitive to the testing set size and the waveband combination used. The results should help an analyst design an appropriate approach for an image classification.