OPTIMIZATION OF TRAINING DATA REQUIRED FOR NEURO-CLASSIFICATION

Classification of remotely sensed data with artificial neural networks is called neuro-classification, and this technique has shown great potential. The amount of data used for training a neural network affects the accuracy and efficiency of the neural network classifier. A neural network was trained separately with 5, 10, 15, and 20 per cent of image data from a Landsat Thematic Mapper scene, which was acquired 29 July 1987 for an agricultural region within Indiana, U.S.A. At a risk level of 5 per cent, the results showed that (a) classifiers NN-5% (neuro-classification with 5 per cent of the image data used for training), NN-10%, and NN-15% did not differ from one another, (b) classifiers NN-15% and NN-20% did not differ from each other, but (c) classifiers NN-5% and NN-10% differed from classifier NN-20%. The training rates were reduced by more than 10 seconds cycle-1 as we increased the percentage of the image data for training a neural network. Approximately 5-10 per cent of the image data are needed to train a neural network classifier adequately to obtain satisfactory performance.