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000005090 084__ $$2WoS$$aComputer Science, Interdisciplinary Applications
000005090 084__ $$2WoS$$aGeosciences, Multidisciplinary
000005090 1001_ $$0P:(DE-Juel1)VDB4989$$aCanty, M. J.$$b0$$uFZJ
000005090 245__ $$aBoosting a Fast Neural Network for Supervised Land Cover Classification
000005090 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2009
000005090 300__ $$a1080 - 1295
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000005090 520__ $$aIt is demonstrated that the use of an ensemble of neural networks for routine land cover classification of multispectral satellite data can lead to a significant improvement in classification accuracy. Specifically, the AdaBoost.M1 algorithm is applied to a sequence of three-layer, feed-forward neural networks. In order to overcome the drawback of long training time for each network in the ensemble, the networks are trained with an efficient Kalman filter algorithm. On the basis of statistical hypothesis tests, classification performance on multispectral imagery is compared with that of maximum likelihood and support vector machine classifiers. Good generalization accuracies are obtained with computation times of the order of I h or less. The algorithms involved are described in detail and a software implementation in the ENVI/IDL image analysis environment is provided. (C) 2009 Elsevier Ltd. All rights reserved.
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000005090 65320 $$2Author$$aAdaptive boosting
000005090 65320 $$2Author$$aKalman filter
000005090 65320 $$2Author$$aSupervised learning
000005090 65320 $$2Author$$aNeural networks
000005090 65320 $$2Author$$aSatellite imagery
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