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Abstract Artificial neural networks are attractive for the classification of remotely sensed data. However, a wide range of factors influence the accuracy with which a data set may be classified. In this paper, the effect of four factors on the accuracy with which agricultural crops may be classified from airborne thematic mapper (ATM) data was investigated. These factors related to the dimensionality of the remotely sensed data, the neural network architecture, and the characteristics of the training and testing sets. A total of 288 classifications were performed and their accuracies evaluated. The artificial neural networks were able to classify the data to high accuracies, with kappa coefficients of up to 0.97 obtained, but the accuracy derived was highly dependent on the factors investigated. A log-linear modelling approach was used to evaluate the simultaneous effect of the factors on classification accuracy. Variations in the dimensionality of the data set, as well as the training and testing set characteristics had a significant effect on classification accuracy. The network architecture, specifically the number of hidden units and layers, did not, however, have a significant effect on classification accuracy in this investigation. This highlights the need to consider a broader set of issues than network architecture when using an artificial neural network for image classification.
Foody et al. (Sat,) studied this question.