Abstract
In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.
Several conclusions are drawn: (1) all models offer similar accuracy but SVM and ABR yield slightly better results than RBFNN; (2) results indicate that ABR are less affected by the curse of dimensionality and has identified efficiently the presence of noisy bands; (3) we find that regularization is a useful method to work with noisy data distributions; and (4) some physical consequences are extracted from the trained models. Finally, this preliminary work lead us to think of kernel-based machines as efficient and robust methods for hyperspectral data classification.
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References
Swain, P.: Fundamentals of pattern recognition in remote sensing. In: Remote Sensing: The Quantitative Approach, pp. 136–188. McGraw-Hill, New York (1978)
Bischof, H., Leona, A.: Finding optimal neural networks for land use classification. IEEE Transactions on Geoscience and Remote Sensing 36, 337–341 (1998)
Bruzzone, L., Fernandez-Prieto, D.: A technique for the selection of kernel function parameters in RBF neural networks for classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 37, 1179–1184 (1999)
Huang, C., Davis, L.S., Townshend, J.R.G.: An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23, 725–749 (2002)
Camps-Valls, G., Gómez-Chova, L., Calpe, J., Soria, E., Martín, J.D., Alonso, L., Moreno, J.: Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Transactions on Geoscience and Remote Sensing 42, 1–13 (2004)
Schölkopf, B., Smola, A.: Learning with Kernels – Support Vector Machines, Regularization, Optimization and Beyond. MIT Press Series, Cambridge (2001)
Rätsch, G., Schökopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.R.: Robust ensemble learning. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 207–219. MIT Press, Cambridge (1999)
Rätsch, G., Mika, S., Schölkopf, B., Müller, K.R.: Constructing boosting algorithms from SVMs: an application to one-class classification. IEEE PAMI (2002. In: press. Earlier version is GMD TechReport No. 119 (2000) (in press)
Müller, K.R., Mika, S., Rätsch, G., Tsuda, K.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12, 181–201 (2001)
Schölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans. Sign. Processing 45, 2758–2765 (1997)
Schapire, R.: The strength of weak learnability. Machine Learning 5, 197–227 (1990)
Gómez-Chova, L., Calpe, J., Soria, E., Camps-Valls, G., Marín, J.D., Moreno, J.: CART-based feature selection of hyperspectral images for crop cover classification. In: IEEE International Conference on Image Processing, Barcelona, Spain (2003)
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Camps-Valls, G., Serrano-López, A.J., Gómez-Chova, L., Martín-Guerrero, J.D., Calpe-Maravilla, J., Moreno, J. (2004). Regularized RBF Networks for Hyperspectral Data Classification. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_53
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DOI: https://doi.org/10.1007/978-3-540-30126-4_53
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