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Evolution of low complexity artificial neural networks for land cover classification from remote sensing data.
| Content Provider | CiteSeerX |
|---|---|
| Author | Schwaiger, Roland Mayer, Helmut A. Huber, Reinhold |
| Abstract | Artificial Neural Networks (ANN) have gained increasing popularity as an alternative to statistical methods for classification of Remote Sensing Data. Their superiority to some of the classical statistical methods has been shown in the literature. Therefore, ANNs are commonly used for segmentation and classification purposes. We address the problem of generating an appropriate low complexity network topology, the right number of training epochs and preprocessing the training data set for multi-layer feed-forward ANNs. A method based on Genetic Algorithms (GA) for the automatic generation of problem--adapted topologies is employed with the parallel netGEN system which has been designed by the authors. A land cover classification problem using multi--spectral Landsat Thematic Mapper (TM) data is presented so as to demonstrate the capabilities of netGEN. 1 Introduction Artificial Neural Networks (ANN) have gained increasing popularity as an alternative to statistical methods for classifi... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Data Land Cover Classification Low Complexity Artificial Neural Network Statistical Method Land Cover Classification Problem Classical Statistical Method Genetic Algorithm Multi Spectral Landsat Thematic Mapper Introduction Artificial Neural Network Automatic Generation Training Data Set Multi-layer Feed-forward Anns Parallel Netgen System Classification Purpose Right Number Artificial Neural Network Appropriate Low Complexity Network Topology |
| Content Type | Text |
