Abstract
A hyperspectral image is used in remote sensing to identify different type of coverts on the Earth surface. It is composed of pixels and each pixel consist of spectral bands of the electromagnetic reflected spectrum. Neural networks and ensemble techniques have been applied to remote sens-ing images with a low number of spectral bands per pixel (less than 20). In this paper we apply different ensemble methods of Multilayer Feedforward networks to images of 224 spectral bands per pixel, where the classification problem is clearly different. We conclude that in general there is an improvement by the use of an ensemble. For databases with low number of classes and pixels the improvement is lower and similar for all ensemble methods. However, for databases with a high number of classes and pixels the improvement depends strongly on the ensemble method. We also present results of classification of support vector machines (SVM) and see that a neural network is a useful alternative to SVM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sadjadi, A., Ghaloum, S., Zoughi, R.: Terrain classification in SAR images using principal component analysis and neural networks. IEEE Trans. On Geoscience and Remote Sensing 31, 511–512 (1993)
Blamire, P.A.: The influence of relative image sample size in training artificial neural networks. International Journal of Remote Sensing 17, 223–230 (1996)
Kumar, A.S., Basu, S.K., Majumdar, K.L.: Robust Classification of Multispectral Data Using Multiple Neural Networks and Fuzzy Integral. IEEE Trans. On Geoscience and Remote Sensing 35(3), 787–790 (1997)
Slade, W.H., Miller, R.L., Ressom, H., Natarajan, P.: Ensemble Neural Network for Satellite-Derived Estimation of Chlorophyll. In: Proceeding of the International Joint Conference on Neural Networks, pp. 547–552 (2003)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science 4(3&4), 385–404 (1996)
Drucker, H., Cortes, C., Jackel, D.: Boosting and Other Ensemble Methods. Neural Computation 6, 1289–1301 (1994)
Freund, Y., Schapire, R.: Experiments with a New Boosting Algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Rosen, B.: Ensemble Learning Using Decorrelated Neural Networks. Connection Science 4(3&4), 373–383 (1996)
Gualtieri, J.A., Chettri, S.R., Cromp, R.F., Johnson, L.F.: Support Vector Mechine Classifiers as Applied to AVIRIS Data. In: Eight JPL Airborne Science Workshop, Summary, pp. 1–11 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernández-Espinosa, C., Fernández-Redondo, M., Torres-Sospedra, J. (2004). Some Experiments on Ensembles of Neural Networks for Hyperspectral Image Classification. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_93
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive