Summary
Classification of multi-source remote sensing and geographic data by neural networks is discussed with respect to feature extraction. Several feature extraction methods are reviewed, including principal component analysis, discriminant analysis, and the recently proposed decision boundary feature extraction method. The feature extraction methods are then applied in experiments in conjunction with classification by multilayer neural networks. The decision boundary feature extraction method shows excellent performance in the experiments.
The authors are very grateful to Dr. Chulhee Lee of the National Institute of Health (NIH) and Prof. David A. Landgrebe of Purdue University for their invaluable assistance in preparing this paper. The authors also thank Dr. Joseph Hoffbeck of AT&T for providing his Matlab data analysis software to us. The Anderson River SAR/MSS data set was acquired, preprocessed, and loaned by the Canada Centre for Remote Sensing, Department of Energy Mines, and Resources, of the Government of Canada. This work was funded in part by the Icelandic Science Council and the Research Fund of the University of Iceland.
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© 1997 Springer-Verlag Berlin Heidelberg
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Benediktsson, J.A., Sveinsson, J.R. (1997). Feature Extraction for Neural Network Classifiers. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_11
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DOI: https://doi.org/10.1007/978-3-642-59041-2_11
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