Texture features for antitank landmine detection using ground penetrating radar

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 7, 2007, Pages 2374-2382

  Torrione, Peter ^b   Collins, Leslie M ^a,b  

^a IEEE

^b Duke University   (United States)

Author keywords

Ground penetrating radar (GPR); Landmines; Relevance vector machine (RVM); Texture features

Indexed keywords

FEATURE EXTRACTION; GROUND PENETRATING RADAR SYSTEMS; OBJECT RECOGNITION; TEXTURES; THREE DIMENSIONAL;

GROUND PENETRATING RADAR (GPR); LANDMINES; RELEVANCE VECTOR MACHINE (RVM);

BOMBS (ORDNANCE);

EID: 34347244506     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.896548     Document Type: Conference Paper

References (48)

1. "Hidden killers: The global landmine crisis," U.S. Dept. State, Bureau Political-Military Affairs, Office Humanitarian Demining Programs, Washington, DC, Tech. Rep., Sep. 1998. [2] "Landmine monitor report, 2004," Human Rights Watch, New York, Tech. Rep., Aug. 2004.
2. Human Rights Watch. (2004, Feb.). Human rights watch: The campaign to ban landmines. [Online]. Available: http://hrw.org/landmines/
3. C. E. Baum, Detection and Identification of Visually Obscured Targets. London, U.K.: Taylor & Francis, 1998.
4. I. L. Morrow and P. V. Genderen, "Effective imaging of buried dielectric objects," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 4, pp. 943-949, Apr. 2002.
5. B. Sai and L. P. Ligthart, "GPR phase-based techniques for profiling rough surfaces and detecting small, low-contrast landmines under flat ground," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 2, pp. 318-326, Feb. 2004.
6. L. Carin, N. Geng, and M. McClure, "Ultra-wideband synthetic aperture radar for mine field detection," IEEE Antennas Propag. Mag., vol. 41, no. 1, pp. 18-33, Feb. 1999.
7. K. Ho and P. Gader, "A linear prediction land mine detection algorithm for hand held ground penetrating radar," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1374-1384, Jun. 2002.
8. L. Collins, L. Huettel, W. Simpson, and S. Tantum, "Sensor fusion of EMI and GPR data for improved landmine detection," in Proc. SPIE-Symp. Detection and Remediation Technologies for Mines and Minelike Targets VII, R. Harmond and G. Dobeck, Eds., Orlando, FL, 2002, vol. 4742, pp. 872-879.
9. K. Ho, L. Collins, L. Huettel, and P. Gader, "Discrimination mode processing for EMI and GPR sensors for hand-held land mine detection," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 1, pp. 249-263, Jan. 2004.
10. S. Perrin, A. Bibaut, E. Duflos, and P. Vanheeghe, "Use of wavelets for ground-penetrating radar signal analysis and multisensor fusion in the frame of land mine detection," in Proc. IEEE Int. Conf. Syst., Man, and Cybern., Oct. 2000, vol. 4, pp. 2940-2945.
11. 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.
12. X. Xu, E. L. Miller, C. M. Rappaport, and G. D. Sower, "Statistical method to detect subsurface objects using array ground-penetrating radar data," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 4, pp. 963-976, Apr. 2002.
13. A. M. Zoubir, I. J. Chant, C. L. Brown, B. Barkat, and C. Abeynayake, "Signal processing techniques for landmine detection using impulse ground penetrating radar," IEEE Sensors J., vol. 2, no. 1, pp. 41-51, Feb. 2002.
14. P. Torrione, C. Throckmorton, and L. Collins, "Performance of an adaptive feature-based processor for a wideband ground penetrating radar system," IEEE Trans. Aerosp. Electron. Syst., vol. 42, no. 2, pp. 644-658, Apr. 2006.
15. P. Gader, W.-H. Lee, and J. N. Wilson, "Detecting landmines with ground penetrating radar using feature based rules, order statistics and adaptive whitening," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 11, pp. 2522-2534, Nov. 2004.
16. P. D. Gader, M. Mystkowski, and Y. Zhao, "Landmine detection with ground penetrating radar using hidden Markov models," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 6, pp. 1237-1244, Jun. 2001.
17. P. Gader, J. Keller, H. Frigui, H. Liu, and D. Wang, "Landmine detection using fuzzy sets with GPR images," in Proc. IEEE Int. Conf. Comput. Intell., Fuzzy Syst., 1998, vol. 1, pp. 232-236.
18. D. Wong and L. Carin, "Analysis and processing of ultra wide-band SAR imagery for buried landmine detection," IEEE Trans. Antennas Propag., vol. 46, no. 11, pp. 1747-1748, Nov. 1998.
19. X. Xu and E. L. Miller, "On the use of contrast stretch and adaptive filter to enhance ground penetrating radar imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jun. 2002, vol. 6, pp. 3585-3587.
20. H. Frigui, P. Gader, and J. Keller, "Fuzzy clustering for land mine detection," in Proc. NAFIPS, Aug. 1998, pp. 261-265.
21. P. D. Gader, J. M. Keller, and B. N. Nelson, "Recognition technology for the detection of buried land mines," IEEE Trans. Fuzzy Syst., vol. 9, no. 1, pp. 31-43, Feb. 2001.
22. P. Gader, R. Grandhi, W.-H. Lee, and J. Wilson, "Feature analysis for the NIITEK ground penetrating radar using order weighted averaging operators for landmine detection," in Proc. SPIE - Symp. Detection and Remediation Technologies for Mines and Minelike Targets IX, R. S. Harmon, J. T. Broach, and J. H. Holoway, Eds., Orlando, FL, 2004, vol. 5415, pp. 953-961.
23. X. Xu, E. L. Miller, and C. M. Rappaport, "Minimum entropy regularization in frequency-wavenumber migration to localize subsurface objects," IEEE Trans. Geonci. Remote Sens., vol. 41, no. 8, pp. 1804-1812, Aug. 2003.
24. C. Chen, M. B. Higgins, K. O'Neill, and R. Detsch, "Ultrawide- bandwidth fully-polarimetric ground penetrating radar classification of subsurface unexploded ordnance," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 6, pp. 1221-1230, Jun. 2001.
25. Q. Zhu and L. Collins, "Applications of feature extraction methods for landmine detection using the Wichmann/NIITEK ground-penetrating radar," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 81-85, Jan. 2005.
26. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification,"IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
27. R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
28. B. van Ginneken and B. M. ter Haar Romeny, "Multi-scale texture classification from generalized locally orderless images," Pattern Recognit., vol. 36, no. 4, pp. 899-911, Apr. 2003.
29. R. Jenssen and T. Eltoft, "Independent component analysis for texture segmentation," Pattern Recognit., vol. 36, no. 10, pp. 2301-2315, Oct. 2003.
30. K. Muneeswarana, L. Ganesanb, S. Arumugamc, and K. Ruba Soundara, "Texture classification with combined rotation and scale invariant wavelet features," Pattern Recognit., vol. 38, no. 10, pp. 1495-1506, Oct. 2005.
31. A. E. Svolos and A. Todd-Pokropek, "Time and space results of dynamic texture feature extraction in MR and CT image analysis," IEEE Trans. Inf. Technol. Biomed., vol. 2, no. 2, pp. 48-54, Jun. 1998.
32. J. Won Jeong and D. Kim, "The design of multi-texture feature vector classifiers for the diagnosis of ultrasound liver images," in Proc. 18th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 1996, pp. 1147-1148.
33. J. Liang, X. Zhao, R. Xu, C. Kwan, and C.-I. Chang, "Target detection with texture feature coding method and support vector machines," in Proc. ICASSP, Montreal, QC, Canada, 2004, pp. II-713-II-716.
34. N. Serrano, A. E. Savakis, and J. Luo, "Improved scene classification using efficient low-level features and semantic clues," Pattern Recognit., vol. 37, no. 9, pp. 1773-1784, Sep. 2004.
35. D. Rohrmus, "Invariant and adaptive geometric textural features for defect detection and classification," Pattern Recognit., vol. 38, no. 10, pp. 1546-1549, Oct. 2005.
36. Y. Liu and S. M. Srihari, "Document binarization based on texture features," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 5, pp. 540-544, May 1997.
37. J. You, W. Li, and D. Zhang, "Hierarchical palmprint identification via multiple feature extraction," Pattern Recognit., vol. 35, no. 4, pp. 847-859, Apr. 2002.
38. P. Huang and S. K. Dai, "Image retrieval by texture similarity," Pattern Recognit., vol. 36, no. 3, pp. 665-679, Mar. 2003.
39. K. Kim, S. Jeong, B. Tae-Chun, J. Y. Lee, and Y. Bae, "Efficient video images retrieval by using local co-occurrence matrix texture features and normalised correlation," in Proc. IEEE TENCON, vol. 2, Aug. 1999, pp. 934-937.
40. B. Manjunath and W. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837-842, Aug. 1996.
41. A. Soffer, "Image categorization using texture features," in Proc. Int. Conf. Document Anal. and Recog., 1997, pp. 233-237.
42. X. Qi and Y. Han, "A novel fusion approach to content-based image retrieval," Pattern Recognit., vol. 38, no. 12, pp. 2449-2465, Dec. 2005.
43. S. Li, J. T. Kwok, H. Zhu, and Y. Wang, "Texture classification using the support vector machine," Pattern Recognit., vol. 36, no. 12, pp. 2883-2893, Dec. 2003.
44. K. I. Kim, K. Jung, S. H. Park, and H. J. Kim, "Support vector machines for texture classification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 11, pp. 1542-1550, Nov. 2002.
45. M. H. Horng, "Texture feature coding method for texture classification," Opt. Eng., vol. 42, no. 1, pp. 228-238, Jan. 2003.
46. J. R. Carr and F. P. de Miranda, "The semivariogram in comparison to the co-occurrence matrix for classification of image texture," IEEE Trans. Geosci. Remote Sens., vol. 36, no. 6, pp. 1945-1952, Nov. 1998.
47. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
48. M. A. Figueiredo, "Adaptive sparseness for supervised learning," IEEE Trans. Pattern Anal Mach. Intell., vol. 25, no. 9, pp. 1150-1159, Sep. 2003.