Abstract

In this letter we present a fully automatic and computationally efficient algorithm based on the Minimum Description Length Principle (MDL) for optimizing multilayer perceptron classifiers. We demonstrate our method on the problem of multispectral Landsat image classification. We compare our results with a hand designed multi-layer perceptron and a Gaussian maximum likelihood classifier where our method produces better classification accuracy with a smaller number of hidden units. 1 Introduction The number of applications of neural networks to remote sensing problems (especially classification) has been constantly increasing in the last few years (e.g. see [1, 2, 3, 4]). It has been demonstrated that in many cases neural networks perform considerably better than classical methods e.g. [1]. However, to achieve this superior performance, the neural networks need to be carefully designed. This includes both the design of the network topology as well as the input/output representati...

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