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Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

Record type: Article

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.

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Citation

Foody, G.M. (2001) Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches Journal of Geographical Systems, 3, (3), pp. 217-232.

More information

Published date: 2001
Keywords: neural networks, remote sensing, classification, jel classification c45 q15 q24

Identifiers

Local EPrints ID: 16142
URI: http://eprints.soton.ac.uk/id/eprint/16142
ISSN: 1435-5930
PURE UUID: cd0ff8d3-d854-4b26-8a61-e93de9aaea61

Catalogue record

Date deposited: 23 Jun 2005
Last modified: 17 Jul 2017 16:44

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