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1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications
In this paper we study neural network overfitting on synthetically generated and real remote sensing data. The effect of overfitting is shown by: 1) visualising the shape of the decision boundaries in feature space during the learning process, and 2) by plotting the classification accuracy of independent test sets versus the number of training cycles. A solution to the overfitting problem is proposed that involves pre-processing the training data. The method relies on obtaining an increase ofdoi:10.1109/igarss.1995.521718 fatcat:427ofokibzcpxlzlcdgshztnqa