volume | PIER Journals

Abstract

A robust algorithm has been developed for improving the backscattered signal and recognizing the shape of the shallow buried metallic object using Artificial Neural Network (ANN) and image analysis techniques for remote sensing at X-band. An ANN with image analysis technique based on tangent analysis is proposed to recognize the shape of metallic buried objects and minimize the orientation effect of buried object. The experimental setup has been assembled for detecting the buried metallic objects of any size at different depths in the sand pit. The system uses only one pyramidal horn antenna for transmitting and receiving microwave signals at X-band (10.0 GHz). All the data to be processed by this algorithm has been received by moving the transmitter/receiver to different locations at a single frequency in X-band in the far field region. ANN technique has been found to be very efficient. An effective training technique has been used to improve the effectiveness of the algorithm. The retrieved result of shape is in good agreement with original shape.

1. Ulbay, F. T., R. K. Moore, and A. K. Fung, Microwave Remote Sensing (Active and Passive), Vol. 3, First Ed., First Ed., Vol. 3, Ch. 14, Addision-Wesley, New York, 1981.

2. Al-Nuaimy, W., Y. Huang, M. Nakhkash, M. T. C. Fang, V. T. Nguyen, and A. Eriksen, "Automatic detection of buried utilites and solid objects with GPR using neural networks and pattern recognition," Journal of Applied Geophysics, Vol. 43, 157-165, 2000.
doi:10.1016/S0926-9851(99)00055-5

3. Carosi, S. and G. Cevini, "An electromagnetic approach based on neural networks for the GPR investigation of buried cylinders," IEEE Geoscience and Remotes Sensing Letters, Vol. 2, No. 1, 2005.

4. Brunzell, H., "Detection of shallowly buried objects using impulse radar," IEEE Trans. Geosci. and Remote Sensing, Vol. 32, No. 2, 875-886, March 1999.
doi:10.1109/36.752207

5., Yamaguchi, Y., Y. Maruyama, A. Kawakami, M. Sengoku, and T. Abe, "Detection of object buried in wet snowpack by FM-CW radar," IEEE Trans. Geosci. and Remote Sensing, Vol. 29, No. 2, 201-208, March 1991.
doi:10.1109/36.73660

6. Franceschetti, G. and R. Lanari, Synthetic Aperture Radar Processing, CRC Press, 1999.

7. Yamaguchi, Y., M. Mitsumoto, M. Sengoku, and T. Abe, "Synthetic aperture FM-CW radar applied to the detection of objects buried in snowpack," IEEE Trans. Geosci. and Remote Sensing, Vol. 32, No. 1, 11-18, January 1994.
doi:10.1109/36.285184

8. Carine, L., R. Kapoor, and C. E. Baum, "Polarimetric SAR imaging of buried landmines," IEEE Trans. Geosci. and Remote Sensing, Vol. 36, No. 6, 1985-1988, November 1998.
doi:10.1109/36.729373

9. Christodoulou, C. and M. Georgiopoulos, Application of Neural Networks in Electromagnetics, Artech House, Boston, London, 2001.

10. Yoshida, T. and S. Omatu, "Neural network approach to land cover mapping," IEEE Trans. Geosci. and Remote Sensing, Vol. 32, No. 5, 1103-1109, September 1994.
doi:10.1109/36.312899

11. Tsintikidis, D., J. L. Haferman, E. N. Anagnostou, W. F. Karjewski, and T. F. Smith, "A Neural network approach to estimating rainfall from spaceborne microwave data," IEEE. Geosci. and Remote Sensing, Vol. 35, No. 5, 1079-1093, September 1997.
doi:10.1109/36.628775

12. Bischof, H. and A. Leonardis, "Finding optimal neural networks for land use classification," IEEE Trans. Geosci. and Remote Sensing, Vol. 36, No. 1, 337-341, January 1998.
doi:10.1109/36.655348

13. Morrow, I. L. and P. Gendern, "Effective imaging of buried dielectric object," IEEE Trans. Geosci. and Remote Sensing, Vol. 40, 943-949, 2002.
doi:10.1109/TGRS.2002.1006383

14. Sullivan, A., R. Damarla, N. Geng, Y. Dong, and L. Carin, "Ultrawide-band synthetic aperture radar for detection of unexploded ordinance: Modeling and measurement," IEEE Trans. Antennas Propagat., Vol. 48, 1306-1315, September 2000.
doi:10.1109/8.898763

15. Tiwari, K. C., D. Singh, and M. K. Arora, "Development of a model for detection and estimation of depth of shallow buried nonmetallioc landmine at microwave X-bang frequency," Progress In Electromagnetic Research, PIER 79, 2008.

16. Currie, N. C., Editor, Radar Reflectivity Measurement: Techniques and Application, Artech House, Norwood, MA, 1989.

17. Ulbay, F. T., R. K. Moore, and A. K. Fung, Active and Passive Remote Sensing, Vol. 1, Artech House, Norwood, MA, 1982.

18. Simon, H., Neural Networks, Prentice Hall, New Jersey, 2001.

19. Zurada, J. M., Introduction to Artificial Neural Systems, 2nd edition, Ch. 4, 163-219, Jaico Publishing House, Mumbai, 1997.

20. Hassoun, M. M., Fundamentals of Artificial Neural Networks, Ch. 6, 284-295, Prentice-Hall of India, New Delhi, 1999.

21. Abdou, I. E. and W. K. Pratt, "Quantative design and evaluation of enhancement/thresholding edge detector," Proceedings of the IEEE, Vol. 67, No. 5, 753-763, 1996.
doi:10.1109/PROC.1979.11325

Dr. Dharmendra Singh

Dr. N. K. Choudhary

Dr. Kailash Chandra Tiwari

Dr. Rajendra Prasad