Integration of neural network and statistical image classification for land cover mapping

Artificial neural networks and statistical classifiers both give good performance in image classification. Since the two methods are based on significantly different mathematical approaches and have complementary capabilities, a useful solution for optimizing performance is to combine them. A method is presented to integrate both types of classifier. In this method both neural network and maximum-likelihood classifiers are initially trained concurrently with the same data set. A second neural network is then trained using only pixels for which the two classifiers did not initially agree. This second network is thus trained specifically to discriminate ambiguous pixels. In the actual classification a simple procedure is adopted to decide which of the classifiers is best to use for a given pixel