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2013-04-02
Fast Detection of GPR Objects with Cross Correlation and Hough Transform
By
Progress In Electromagnetics Research C, Vol. 38, 229-239, 2013
Abstract
A GPR object detection algorithm delivers a promising performance using the Hough transform through a high computational load. This paper presents a fast Hough-based algorithm. To reduce the parameter space of the Hough transform, first, two parameters for a reflection hyperbola were estimated using cross correlation between adjacent A-scans. Next, only a 1D Hough transform is necessary to detect an object compared with the 3D transform, which comprises the traditional Hough-based methods. Our method is compared with three other detection methods using field data. The results show that the proposed method has an encouraging detection ability and high computational efficiency.
Citation
Jian Wang, and Yi Su, "Fast Detection of GPR Objects with Cross Correlation and Hough Transform," Progress In Electromagnetics Research C, Vol. 38, 229-239, 2013.
doi:10.2528/PIERC13022510
References

1. Jol, H. M., Ground Penetrating Radar Theory and Applications, Elsevier Science, Amsterdam, 2009.

2. Carlotto, M. J., "Detecting buried mines in ground penetrating radar using a hough transform approach," Battlespace Digitization and Network-Centric Warfare II, 251-261, Orlando, 2002.
doi:10.1117/12.478719

3. Chen, D., C. Huang, and Y. Su, "An integrated method of statistical method and hough transform for GPR target S detection and location," Acta Electronic Sinica, Vol. 32, No. 9, 1468-1471, 2004.

4. Simi, A., S. Bracciali, and G. Manacorda, "Hough transform based automatic pipe detection for array GPR: Algorithm development and on-site tests," IEEE Radar Conference, 1-6, Rome, 2008.

5. Birkenfeld, S., "Automatic detection of reflexion hyperbolas in GPR data with neural networks," World Automation Congress, 1-6, Kobe, 2010.

6. Liu, Y., M. Wang, and Q. Cai, "The target detection for GPR images based on curve fitting," 3rd International Congress on Image and Signal Processing, 2876-2879, Yantai, 2010.

7. Guil, N., J. Villalba, and E. L. Zapata, "A fast hough transform for segment detection," IEEE Trans. on Image Process., Vol. 4, No. 11, 1541-1548, 1995.
doi:10.1109/83.469935

8. Kiryati, N., Y. Eldar, and A. M. Bruckstein, "A probabilistic hough transform," Pattern Recognition, Vol. 24, No. 4, 303-316, 1991.
doi:10.1016/0031-3203(91)90073-E

9. Long, K., P. Liatsis, and N. Davidson, "Image processing of ground penetrating radar data for landmine detection," Proc. of SPIE, Vol. 6217, 62172R1-62172R12, 2006.

10. Hayashi, N. and M. Sato, "F-K filter designs to suppress direct waves for bistatic ground penetrating radar," IEEE Trans. on Geosci. Remote Sensing, Vol. 48, No. 3, 1433-1444, 2010.
doi:10.1109/TGRS.2009.2032536

11. Gader, P. D., M. Mystkowski, and Y. Zhao, "Landmine detection with ground penetrating radar using hidden Markov models," IEEE Trans. on Geosci. Remote Sensing, Vol. 39, 1231-1244, 2001.
doi:10.1109/36.927446

12. Wilson, J. N., P. Gader, W.-H. Lee, H. Frigui, and K. C. Ho, "A large-scale systematic evaluation of algorithms using ground-penetrating radar for landmine detection and discrimination," IEEE Trans. on Geosci. Remote Sensing, Vol. 45, No. 8, 2560-2572, 2007.
doi:10.1109/TGRS.2007.900993

13. Zhu, Q. and L. M. Collins, "Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar," IEEE Trans. on Geosci. Remote Sensing, Vol. 43, No. 1, 81-85, 2005.
doi:10.1109/TGRS.2004.839431