The effect of training set size and composition on artificial neural network classification

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Abstract
Training set characteristics can have a significant effect on the performance of an image classification. In this paper the effect of variations in training set size and composition on the accuracy of classifications of synthetic and remotely sensed data sets by an artificial neural network and discriminant analysis are assessed. Attention is focused on the effects of variations in the overall size of the training set, in terms of the number of training samples, as well as on variations in the size of individual classes in the training set. The results showed that higher classification accuracies were generally derived from the artificial neural network, especially when small training sets only were available. It was also apparent that the opportunity of the artificial neural network to learn class appearance was influenced by the composition of the training set. The results indicated that the size of each class in the training set had an effect similar to. that of including a priori probabilities of class membership into the discriminant analysis. In the classification of the remotely sensed data set the classification accuracy was increased significantly as a result of increasing the number of training cases for abundant classes in the image.