Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches
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), 217-232.
Full text not available from this repository.
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.
|Keywords:||neural networks, remote sensing, classification, jel classification c45 q15 q24|
|Subjects:||G Geography. Anthropology. Recreation > G Geography (General)|
|Divisions:||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Date Deposited:||23 Jun 2005|
|Last Modified:||27 Mar 2014 18:06|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)