Ultrawideband synthetic aperture radar landmine detection

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 11, 2007, Pages 3561-3572

  Jin, Tian ^a,b   Zhou, Zhimin ^b  

^a IEEE   (China)

^b NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Evidence framework; Fuzzy hypersphere support vector machine (FHSSVM); Landmine; Space wavenumber processing; Synthetic aperture radar (SAR); Ultrawideband (UWB)

Indexed keywords

ANTITANK MINES; EVIDENCE FRAMEWORK; FUZZY HYPERSPHERE SUPPORT VECTOR MACHINE (FHSSVM); LANDMINES; SPACE-WAVENUMBER PROCESSING; ULTRAWIDEBAND (UWB);

FEATURE EXTRACTION; FUZZY SETS; SCATTERING; SUPPORT VECTOR MACHINES;

SYNTHETIC APERTURE RADAR;

EID: 35648937032     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.906138     Document Type: Conference Paper

References (30)

1. I. Oppermann, M. Hamalainen, and J. Iinatti, UWB Theory and Applications. Chichester, U.K.: Wiley, 2004, ch. 1.
2. J. Andrieu, F. Gallais, V. Mallepeyre, V. Bertrand, B. Beillard, B. Jecko et al., "Land mine detection widi an ultra-wideband SAR system," Proc. SPIE, vol. 4742, pp. 237-247, Aug. 2002.
3. J. Kositsky, R. Cosgrove, C. Amazeen, and P. Milanfar, "Results from a forward-looking GPR mine detection system," Proc. SPIE, vol. 4742, pp. 206-217, 2002.
4. M. A. Ressler, "Army Research Laboratory ultra wideband boomSAR," in Proc. Int. Geosci. Remote Sens. Symp., Lincoln, NE, 1996, pp. 1886-1888.
5. R. S. Vickers, "Design and applications of airborne radars in the VHF/UHF band," IEEE Aerosp. Electron. Syst. Mag., vol. 17, no. 6, pp. 26-29, Jun. 2002.
6. L. M. Kaplan, J. H. McClellan, and S.-M. Oh, "Prescreening during image formation for ultrawideband radar," IEEE Trans. Aerosp. Electron. Syst., vol. 38, no. 1, pp. 74-88, Jan. 2002.
7. A. Andrews, J. Ralston, and M. Tuley, "Research on ground-penetrating radar for detection of mines and unexploded ordnance: Current status and research strategy," IDA Document D-2416, Alexandria, VA, 1999.
8. L. Nguyen, K. Kappra, D. Wong, R. Kapoor, and J. Sichina, "A mine field detection algorithm utilizing data from an ultra-wideband wide-area surveillance radar," Proc. SPIE, vol. 3392, pp. 627-643, Sep. 1998.
9. D. Tax, "One-class classification: Concept-learning in the absence of counter-example," Ph.D. dissertation, Delft Univ. Technol., Delft, The Netherlands, Jun. 2001.
10. R. B. Cosgrove, P. Milanfar, and J. Kositsky, "Trained detection of buried mines in SAR images via the deflection-optimal criterion," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 11, pp. 2569-2575, Nov. 2004.
11. Y. Sun and J. Li, "Adaptive learning approach, to landmine detection," IEEE Trans. Aerosp. Electron. Syst., vol. 41, no. 3, pp. 973-985, Jul. 2005.
12. T. Wang, J. M. Keller, P. D. Gader, and O. Sjahputera, "Frequency subband processing and feature analysis of forward-looking groundpenetrating radar signals for land-mine detection," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 3, pp. 718-729, Mar. 2007.
13. T. Jin, Z. Zhou, and W. Chang, "UWBWA SAR imaging modeling and its time-frequency processing," in Proc. 7th Int. Conf. Signal Process., Beijing, China, 2004, pp. 1930-1933.
14. B. Krishnapuram, J. Sichina, and L. Carin, "Physics-based detection of targets in SAR imagery using support vector machines," IEEE Sensors J., vol. 3, no. 2, pp. 147-157, Apr. 2003.
15. T. Jin, Z. Zhou, and W. Chang, "Ultra-wideband SAR time-frequency representation image formation," Proc. Inst. Electr. Eng. - Radar, Sonar Navig., vol. 153, no. 5, pp. 389-395, Oct. 2006.
16. A. R. Webb, Statistical Pattern Recognition, 2nd ed. Chichester, U.K.: Wiley, 2002, ch. 9.
17. C. J. C. Burges, "A tutorial on support vector machine for pattern recognition," Data Mining Knowl. Discovery, vol. 2, no. 2, pp. 121-167, Jun. 1998.
18. R. Rau and J. H. McClellan, "Analytic models and postprocessing techniques for UWB SAR," IEEE Trans. Aerosp. Electron. Syst., vol. 36, no. 4, pp. 1058-1074, Oct. 2000.
19. T. Jin, Z. Zhou, and W. Chang, "Modified wavefront reconstruction imaging formation for stand-off GPEN SAR," Electron. Lett., vol. 41, no. 10, pp. 616-618, May 2005.
20. T. Jin, Z.-.M. Zhou, W-G. Chang, and Q. Song, "A postfilter method for refraction and dispersion effects compensation in GPSAR image," in Proc. CIE Int. Conf. Radar, Shanghai, China, 2006, pp. 1022-1025.
21. L. Coohen, Time-Frequency Analysis: Theory and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1995, ch. 9.
22. L. Carin, R. Kapoor, and C. E. Baum, "Polarimetric SAR imaging of buried landmines," IEEE Trans. Geosci. Remote Sens., vol. 36, no. 6, pp. 1985-1988, Nov. 1998.
23. R. D. Chaney, A. S. Willsky, and L. M. Novak, "Coherent aspectdependent SAR image formation," Proc. SPIE, vol. 2230, pp. 256-274, Jun. 1994.
24. T. Jin, Z.-M. Zhou, Q. Song, and W-G. Chang, "Ultra-wideband SAR subsurface metallic landmine images: Simulation and measurement," in. Proc. CIE Int. Conf. Radar, Shanghai, China, 2006, pp. 1667-1670.
25. C-F. Lin and S.-D. Wang, "Fuzzy support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 464-471, Mar. 2002.
26. D. Waagen, M. Cassabaum, H. Schmitt, and B. Pollock, "Support vector machine optimization via margin distribution analysis," Proc. SPIE, vol. 5094, pp. 348-357, Sep. 2003.
27. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 1995, ch. 4.
28. D. J. C. MacKay, "Bayesian interpolation," Neural Comput., vol. 4, no. 3, pp. 415-447, Mar. 1992.
29. J. T.-Y Kowk, "Evidence framework applied to support vector machines," IEEE Trans. Neural Netw., vol. 1.1, no. 5, pp. 1162-1173, Sep. 2000.
30. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Chichester, U.K.: Wiley, 2001, ch. 9.