SVM-Based Detection of SAR Images in Partially Developed Speckle Noise

Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of SAR (synthetic aperture radar) images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to real SAR images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected SAR images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (the detection hypotheses) in the original images.

Keywords: Least Square-Support Vector Machine, SyntheticAperture Radar. Partially Developed Speckle, Multi-Look Model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070567

References:

[1] O. Chapelle, P. Haffner, and V. Vapnik, "Support Vector Machines for Histogram-Based Image Classification," IEEE Transactions on Neural Networks, vol. 10, no. 5, 1999, pp. 1055-1064.
[2] Y. Zhang, R. Zhao, "Image Classification by Support Vector Machines," Proceedings of 2001 International Symposium on Intelligent Multimedia, and Speech Processing, Hong Kong, 2001, pp. 360-363.
[3] Y. Wang and H. Zhang, "Content-Based Image Orientation Detection with Support Vector Machines," IEEE Workshop on Content-Based Access of Image and Video Libraries, 2001, pp. 17-23.
[4] J. Daba and M. Bell, "Statistics of the Scattering Cross-Section of a Small Number of Random Scatterers," IEEE Transaction on Antennas and Propagation, 1994, pp. 773-783.
[5] E. Christensen and M. Dich, "SAR Antenna Design for Ambiguity and Multipath Suppression," IEEE Transaction on Geosciences & Remote Sensing, vol. 29, no. 3., 1993.
[6] V. Vapnik, "Estimation of Dependences Based on Empirical Data," Nauka English Translation, Springer Verlag, 1982.
[7] N. Christianini and J. Taylor, "Support Vector Machine and Other Kernel Learning Methods". London: Cambridge University Press, 2003.
[8] J. Christopher and C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition", Kulwer Publishers, vol. 12, no. 2, 1998, pp. 121-167.
[9] J. Weston and C. Watkins, "Support Vector Machines from Multi-Class Pattern Recognition," University Of London, unpublished.
[10] T. Joachims, "Support Vectors and Kernel Methods", Cornell University, unpublished.
[11] J. Suykers and J. Vardewalle, "Multi-Class Least Square-Support Vector Machine," Universite Catholique de Louvain, Belgium, unpublished.
[12] C. Hsu and C. Lin, "A Comparison of Methods for Multi-Class Support Vector Machines," IEEE Trans. Neural Net., vol. 13, 2002, pp. 415- 425.
[13] D. Snyder and M. Miller, Random Point Processes in Time and Space. New York: Springer-Verlag, 1991.
[14] K. Pelckmans, J. Suykens, T. Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor, and J. Vandewalle, "LS-SVMlab Toolbox User-s Guide version 1.5", Katholiede Univeristeit Leuven, Belgium, unpublished. Available http://www.esat.kuleuven.ac.be/sista/lssvmlab