Abstract
Texture provides valuable information for synthetic aperture radar (SAR) image classification, especially when the single-band and single-polarized SAR is concerned. Three texture feature extraction methods including the gray-level co-occurrence matrix; the gray-gradient co-occurrence matrix and the energy measures of the undecimated wavelet decomposition are introduced to represent the textural information of SAR image. However, the simple combination of these features with each other is usually not suitable for SAR image classification due to the resulting redundancy and the additive computation complexity. Based on immune clonal selection algorithm, a new feature selection approach characterized by rapid convergence to global optimal solution is proposed and applied to find the optimal feature subset. Based on the features selected, SVMs are used to classify the land covers in SAR images. The effectiveness of feature subset selected and the validity of the proposed method are well verified by the experiment results.
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
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. on System, Man, and Cybernetics 3, 610–621 (1973)
Solberg, A.H.S., Jain, A.K.: Texture Fusion and Feature Selection Applied to SAR Imagery. IEEE Trans. on Geoscience and Remote Sensing 35, 475–479 (1997)
Peleg, S., Naor, J., Hartley, R., Avnir, D.: Multiple Resolution Texture Analysis and Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 518–523 (1984)
Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Trans. on Intelligent Systems 13, 44–49 (1998)
Jiao, L.C., Du, H.F.: Development and Prospect of the Artificial Immune System. Acta Electronica Sinica 31, 73–80 (2003)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Fukuda, S., Hirosawa, H.: A Wavelet-Based Texture Feature Set Applied to Classification of Multifrequency Polarimetric SAR Images. IEEE Trans. on Geoscience and Remote Sensing 37, 2282–2286 (1999)
Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence Journal 97, 273–324 (1997)
Sun, Z.H., Yuan, X.J., Bebis, G., Louis, S.J.: Neural-Network-based Gender Classification Using Genetic Search for Eigen-Feature Selection. IEEE International Joint Conference on Neural Networks 3, 2433–2438 (2002)
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
Zhang, X., Shan, T., Jiao, L. (2004). SAR Image Classification Based on Immune Clonal Feature Selection. 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_62
Download citation
DOI: https://doi.org/10.1007/978-3-540-30126-4_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
eBook Packages: Springer Book Archive