Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches |
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| Authors: | Giles M Foody |
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| Institution: | (1) Department of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, UK (e-mail: g.m.foody@soton.ac.uk), GB |
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| Abstract: | Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused
on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes
be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN)
were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%.
The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership
but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification
analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class,
to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each
of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements
of the application in-hand.
Received: 23 March 2000 / Accepted: 9 July 2000 |
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| Keywords: | : Neural networks remote sensing classification |
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