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30 June 1994 Combination of parametric and neural classifiers for analysis of multisensor remote sensing imagery
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The classification of remotely sensed satellite data for land surface mapping is a complex pattern recognition problem. Recent work has shown that neural networks often perform better than parametric or statistical classifiers in a large number of cases. Since neural and parametric classifiers are based on very different mathematical models it is appropriate to attempt to integrate them in order to exploit the best aspects of both. A simple method for integrating neural and statistical classifiers effectively is proposed in this paper. This method has been developed with the aim of improving land cover map products derived from multi- sensor data sets. The integration is achieved in a multi-stage process in which two classifiers of different types are initially trained to classify the same multi-sensor training data, and then samples for which the two classifiers are in disagreement are used to train an additional second stage neural classifier. Preliminary results show that significant improvements can be made in overall classification performance compared to using either neural or parametric classifiers alone.
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Ioannis Kanellopoulos, Freddy Fierens, and Graeme G. Wilkinson "Combination of parametric and neural classifiers for analysis of multisensor remote sensing imagery", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994);

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