Hard and soft classifications by a neural network with a non-exhaustively defined set of classes

Many assumptions are typically made in the course of a supervised digital image classification. The focus of this paper is the commonly made assumption of an exhaustively defined set of classes. This assumption is often unsatisfied, with the imagery containing regions of classes that were not included in training the classification. The failure to satisfy the assumed condition was investigated with reference to hard and soft land cover classifications by a feedforward neural network. The accuracy of these classifications was decreased if a non-exhaustively defined set of classes was used. The exclusion of a class from the training stage resulted in a decrease in the accuracy of a hard classification of agricultural crops of up to 21.2%. Moreover, there were marked differences between the 'real' and 'apparent' accuracies of classifications of up to 15.4%. With soft classifications of urban land cover, the presence of an untrained class also degraded classification accuracy. In the soft classification output, the correlation between the actual and estimated proportional cover of a class declined from r =0.97 to r =0.76 when another class was excluded from the training stage of the classification. Possible means to reduce the negative impacts of untrained classes are considered briefly. Post-classification thresholding of the neural network's output unit activation levels may form the basis of a method to identify and remove cases of an untrained class from a hard classification. Alternatively, supporting information on the typicality of class membership may be used to identify cases representing an area containing an untrained class in both hard and soft classifications and this is illustrated with reference to a soft classification.