Using prior knowledge in artificial neural network classification with a minimal training set

Prior knowledge of class occurrence can be used in conventional statistical classifications to increase classification accuracy significantly. These classifications, however, require typically a large training set for their correct use. When small training sets are available therefore an alternative classification technique is required and one increasingly popular alternative is the use of artificial neural network techniques. Whilst many comparative studies have shown artificial neural network classifications to be more accurate than those derived from conventional statistical classifiers the difference in accuracy can be more than made-up if prior knowledge was incorporated in the statistical classification. To make the full use of artificial neural networks therefore approaches for the incorporation of prior knowledge must be developed. This paper proposes one approach which may be used and illustrates its use in the classification of agricultural crops from synthetic aperture radar data with a minimal training set. The results show that whilst the artificial neural network could accurately learn the training data the classification of an independent testing set was poor, an accuracy of only 27.0 per cent was derived for a seven class classification. Incorporating prior knowledge, however, significantly increased classification accuracy to 58.4 per cent. This latter result was comparable, in terms of accuracy and the pattern of class allocation, to a classification of the same data set by a discrimination analysis with prior knowledge.