Probabilistic neural network architecture for high-speed classification of remotely sensed imagery

Abstract

In this article we discuss a neural network architecture (the probabilistic neural net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised nonparametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network (BPNN), which can be shown to the O( N 6) time for training where N is the dimensionality of the input vector. In addition, the PNN can be implemented in a feed-forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the article. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the BPNN. The PNN is shown to be better than GMLC and not as good as the BPNN with regard to classification accuracy.