SVM-Based Detection of SAR Images in Partially Developed Speckle Noise
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.
[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
[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
@article{"International Journal of Information, Control and Computer Sciences:57347", author = "J. P. Dubois and O. M. Abdul-Latif", title = "SVM-Based Detection of SAR Images in Partially Developed Speckle Noise", 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.", volume = "1", number = "12", pages = "3902-5", }