volume | PIER Journals

2007-10-27

Development of a Model for Detection and Estimation of Depth of Shallow Buried Non-Metallic Landmine at Microwave X-Band Frequency

Kailash Chandra Tiwari,

Dr. Kailash Chandra Tiwari

Department of Civil Engineering

Indian Institute of Technology Roorkee

India

Homepage

Dharmendra Singh,

Dr. Dharmendra Singh

Department of Electronics and Computer Engineering

Indian Institute of Technology Roorkee

India

Homepage

Manoj Arora

Dr. Manoj Arora

Department of Civil Engineering

Indian Institute of Technology Roorkee

India

Homepage

, Vol. 79, 225-250, 2008

doi:10.2528/PIER07100201

Abstract

Detection andestimation of depth of shallow buried landmines using microwave remote sensing is a complex and computationally intensive task. Despite a lot of research to correctly locate and identify the buried landmines, and to estimate its depth using microwave remote sensing data which is essential for demining with minimal risk, a lot of uncertainties still exist. Therefore in this paper, an extensive study using a groundbasedX-bandscatterometer for detection of shallow buried landmine and estimation of its depth has been carriedout. An experimental setup consisting of a ground basedscatterometer operating in microwave X-band(10 GHz, 3 cm) has been usedto generate backscatter data in a gridof 24×24 and a series of experiments under laboratory conditions conducted using dummy landmines (without explosives) buried to different depths up to 10 cm in dry smooth sand. It is difficult to detect the buried landmine by visual inspection of the raw data; therefore a novel approach by fusion of image processing techniques with electromagnetic (EM) analysis has been evolvedfor detection andestimation of depth of the landmine. The raw data generated through the experiments was processedthrough a series of image processing steps and a region of interest segmentedusing Otsu andmaxim um entropy based thresholding methods for further processing. A detection figure test has been proposedat this stage to reduce false alarms. Genetic algorithm (GA) with an electromagnetic (EM) model fusion has been proposedto estimate the depth after segmenting a suspectedregion containing the mine. The main advantage of the proposed model is that it does not have any requirement of separate training and test data set to train the optimizer and validate the results. Analyses of the results indicate that it is possible to segment suspected region of interest containing the landmines in data obtained in microwave Xband using either of the two thresholding methods. The depth of buriedland mines estimatedusing the proposed GA optimized EM model was also foundto be in goodagreemen t with the actual depth. The proposedanalysis is expectedto be extremely useful in future in detection and estimation of the depth of landmines using satellite data in microwave X-band.

Citation

Copy Export

Kailash Chandra Tiwari, Dharmendra Singh, and Manoj Arora, "Development of a Model for Detection and Estimation of Depth of Shallow Buried Non-Metallic Landmine at Microwave X-Band Frequency," , Vol. 79, 225-250, 2008.
doi:10.2528/PIER07100201

References

1. Bureau of Political & Military Affairs "Hidden killers," US Department of State Publication 10575, No. 9, 1998.

2. Potin, D. andP . Vanheeghe, "An abrupt change detection algorithm for buriedland mine localization," IEEE Transactions on Geosciences And Remote Sensing, Vol. 44, No. 2, 2006.
doi:10.1109/TGRS.2005.861413

3. Maathuis, B. H. P. andJ. L. Van Genderen, "A review of satellite andairb orne sensors for remote sensing basedd etection of minefields and landmines," Intl. Journal of Remote Sensing, No. 12, 2004.

4. Druyts, P., Y. Yvinee, and andM. Acheroy, "Usefulness of semi-automatic tools for airborne minefieldd etection," Signal andImage Centre.

5. Gader, P. D. et al., "Recognition technology for the detection of buriedland mines," IEEE Transactions Fuzzy Sys., Vol. 9, No. 1, 2001.

6. Carosi, S. andG. Cevini, "An electromagnetic approach basedon neural networks for the GPR investigation of buriedcylind ers," IEEE Geosciences and Remote Sensing Letters, Vol. 2, No. 1, 2005.

7. Collins. L., et al., "A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection," IEEE Transactions on Fuzzy Systems, Vol. 9, No. 1, 2004.

8. Bermani, E. et al., "An innovative real time technique for buried object detection," IEEE Transactions on Geosciences and Remote Sensing, Vol. 41, No. 4, 2003.
doi:10.1109/TGRS.2003.810928

9. Gader, et al., "Landmine detection with ground penetrating radar using hidden Markov models," IEEE Transactions Geosciences and Remote Sensing, Vol. 41, No. 4, 2001.

10. Xu, X., et al. "Statistical methodto detect subsurface objects using array ground penetrating radar data," IEEE Transaction on Geosciences and Remote Sensing, Vol. 40, No. 4, 2002.

11. Johnson, J. T. and R. J. Burkholder, "A study of scattering from an object below a rough surface," IEEE Transactions on Geosciences and Remote Sensing, Vol. 42, No. 1, 2004.
doi:10.1109/TGRS.2003.815670

12. Daniels, J., et al. "Microwave remote sensing of physically buriedob jects in Negev desert: Implications for subsurface martiani exploration," Journal of Geophysical Research, Vol. 108, No. 48033, 2003.

13. Ulaby, F. T., R. K. Moore, and andA. K. Fung, Radar Remote Sensing and Surface Scattering Emission Theory, Vols. II & III, 1982.

14. Brooks, J. W., "The detection of buried non-metallic antipersonnel landmines," Dissertation submitted at University of Alabama, 2000.

15. Petrou, M. andP . Bosdogianni, Image Processing — The Fundamentals, John Wiley & Sons, 1999.

16. Tian, H., et al. "Implementing Otsu's thresholding process using area-time efficient logarithmic approximation unit," 0-7803-7761-3/03C2003IEEE, No. 3, 0-7803, 2003.

17. Wong, A. K. C. andP . K. Sahoo, "A gray level threshold selection methodbasedon maximum entropy principle," IEEE Transactions on Systems, Vol. 19, No. 4, 1989.

18. Johnson, M., et al. "Genetic algorithms in engineering electromagnetics," IEEE Antennas and Propagation Magazine, Vol. 39, No. 4, 1997.
doi:10.1109/74.632992

19. Chen, X., D. Liang, and andK. Huang, "Microwave imaging 3-D buriedob jects using parallel genetic algorithm combinedwith FDTD technique," J. of Electromagn. Waves and Appl., Vol. 20, No. 13, 1761-1774, 2006.
doi:10.1163/156939306779292264

20. Van den Bosch, I., "Accurate and efficient modelling of monostatic GPR signal of dielectric targets buriedin stratifiedmed ia," J. of Electromagn. Waves and Appl., Vol. 20, No. 3, 283-290, 2006.
doi:10.1163/156939306775701704

21. Nishimoto, M., S. Ueno, and andY. Kimura, "Feature extraction from GPR data for identification of landmine like objects under rough groundsurface," J. of Electromagn. Waves and Appl., Vol. 20, No. 12, 1577-1586, 2006.
doi:10.1163/156939306779292318

22. Pingenot, J., "Full wave analysis of RF signal attenuation in a lossy rough surface cave using a high order time domain vector finite element method," J. of Electromagn. Waves and Appl., Vol. 20, No. 12, 1695-1705, 2006.
doi:10.1163/156939306779292408

23. Chen, H.-T. andG.-Q. Zhu, "Model the electromagnetic scattering from three-dimensional PEC object buried under rough groundb y MOM andmo difiedPO hybridmetho d," Progress In Electromagnetics Research, Vol. 77, 15-27, 2007.
doi:10.2528/PIER07072202

24. Xue, W. andX.-W. Sun, "Multiple targets detection method basedon binary Hough transformation andad aptive time frequency filtering," Progress In Electromagnetics Research, Vol. 74, 309-317, 2007.
doi:10.2528/PIER07051406

25. Bermani, E. andA. Boni, "A multi source strategy basedon a learning by examples technique for buriedob ject detection," Progress In Electromagnetics Research, Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701

26. Golestani-Rad, L. and J. Rashed-Mohassel, "Rigorous analysis of EM-wave penetration into a typical room using FDTD method: The transfer function concept," J. of Electromagn. Waves and Appl., Vol. 20, No. 7, 913-926, 2006.
doi:10.1163/156939306776149851