Boosting a fast neural network for supervised land cover classification

Authors:
Morton J. Canty
Affiliations:
Institute for Chemistry and Dynamics of the Geosphere, Jülich Research Center, D-52425 Jülich, Germany
Venue:
Computers & Geosciences
Year:
2009

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Abstract

It 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 1h or less. The algorithms involved are described in detail and a software implementation in the ENVI/IDL image analysis environment is provided.