Contributions to automatic target recognition systems for underwater mine classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 1, 2015, Pages 505-518

  Fei, Tai ^a   Kraus, Dieter ^b   Zoubir, Abdelhak M ^c  

^a HELLA KGAA HUECK AND CO   (Germany)

^b HOCHSCHULE BREMEN   (Germany)

^c DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Automatic target recognition (ATR); Dempster Shafer theory (DST); Ensemble learning; Filter method for feature selection; Synthetic aperture sonar (SAS)

Indexed keywords

AUTOMATIC TARGET RECOGNITION; SYNTHETIC APERTURE SONAR;

BASIC BELIEF ASSIGNMENT; CLASSIFICATION PERFORMANCE; CLASSIFICATION RESULTS; COMPREHENSIVE ASSESSMENT; DEMPSTER-SHAFER THEORY; ENSEMBLE LEARNING; FILTER METHOD; SYNTHETIC APERTURE SONAR (SAS);

FEATURE EXTRACTION;

AUTOMATION; IMAGE CLASSIFICATION; MINE; PATTERN RECOGNITION; UNDERWATER ENVIRONMENT;

EID: 84906781303     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2324971     Document Type: Article

References (65)

1. S. Johnson and A. Deaett, "The application of automated recognition techniques to side-scan sonar imagery," IEEE J. Ocean. Eng., vol. 19, no. 1, pp. 138-144, Jan. 1994.
2. L. Henriksen, "Real-time underwater object detection based on an electrically scanned high-resolution sonar," in Proc. Symp. Auton. Underwater Veh. Technol., 1994, pp. 99-104.
3. R. Fandos and A. M. Zoubir, "Optimal feature set for automatic detection and classification of underwater objects in SAS images," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 454-468, Jun. 2011.
4. J. E. Piper, R. Lim, E. I. Thorsos, and K. L. Williams, "Buried sphere detection using a synthetic aperture sonar," IEEE J. Ocean. Eng., vol. 34, no. 4, pp. 485-494, Oct. 2009.
5. M. Hayes and P. Gough, "Synthetic aperture sonar: A review of current status," IEEE J. Ocean. Eng., vol. 34, no. 3, pp. 207-224, Jul. 2009.
6. J. Piper, K. Commander, E. Thorsos, and K. Williams, "Detection of buried targets using a synthetic aperture sonar," IEEE J. Ocean. Eng., vol. 27, no. 3, pp. 495-504, Jul. 2002. (Pubitemid 35313399)
7. V. Myers and D. Williams, "Adaptive multiview target classification in synthetic aperture sonar images using a partially observable Markov decision process," IEEE J. Ocean. Eng., vol. 37, no. 1, pp. 45-55, Jan. 2012.
8. R. Fandos, A. M. Zoubir, and K. Siantidis, "Unified design of a feature based ADAC system for mine hunting using synthetic aperture sonar," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2413-2426, May 2014.
9. T. Fei and D. Kraus, "An expectation-maximization approach assisted by Dempster-Shafer theory and its application to sonar image segmentation," in Proc. IEEE ICASSP, 2012, pp. 1161-1164.
10. R. Fandos and A. M. Zoubir, "Enhanced initialization scheme for a three-region Markovian segmentation algorithm and its application to SAS images," in Proc. 10th ECUA, 2010, vol. 3, pp. 1323-1331.
11. S. Reed, Y. Petillot, and J. Bell, "An automatic approach to the detection and extraction of mine features in sidescan sonar," IEEE J. Ocean. Eng., vol. 28, no. 1, pp. 90-105, Jan. 2003.
12. M. Yang, K. Kpalma, and J. Ronsin, A Survey of Shape Feature Extraction Techniques. InTech, 2008, ch. 3, pp. 43-90.
13. Y. Tang, B. Li, H. Ma, and J. Lin, "Ring-projection-wavelet-fractal signatures: A novel approach to feature extraction," IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., vol. 45, no. 8, pp. 1130-1134, Aug. 1998. (Pubitemid 128745275)
14. J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-6, no. 4, pp. 269-285, Apr. 1976.
15. R. B. Yadav, N. K. Nishchal, A. K. Gupta, and V. K. Rastogi, "Retrieval and classification of shape-based objects using Fourier, generic Fourier, wavelet-Fourier descriptors technique: A comparative study," Opt. Laser Eng., vol. 45, no. 6, pp. 695-708, Jun. 2007.
16. D. Zhang and G. Lu, "Shape-based image retrieval using generic Fourier descriptor," Signal Process., Image Commun., vol. 17, no. 10, pp. 825-848, Nov. 2002. (Pubitemid 35282149)
17. M. Amadasun and R. King, "Textural features corresponding to textural properties," IEEE Trans. Syst., Man, Cybern., vol. 19, no. 5, pp. 1264-1274, Sep./Oct. 1989.
18. M. Dash and H. Liu, "Feature selection for classification," Intell. Data Anal., vol. 1, no. 1-4, pp. 131-156, 1997.
19. H. H. Yang and J. Moody, "Feature selection based on joint mutual information," in Proc. Int. ICSC Symp. AIDA, 1999, pp. 22-25.
20. D. A. Bell and H. Wang, "A formalism for relevance and its application in feature subset selection," Mach. Learn., vol. 41, no. 2, pp. 175-195, Nov. 2000.
21. G. Brown, A. Pocock, M. J. Zhao, and M. Luján, "Conditional likelihood maximisation: A unifying framework for information theoretic feature selection," J. Mach. Learn. Res., vol. 13, no. 1, pp. 27-66, Jan. 2012.
22. R. Battiti, "Using mutual information for selecting features in supervised neural net learning," IEEE Trans. Neural Netw., vol. 5, no. 4, pp. 537-550, Jul. 1994.
23. N. Kwak and C. H. Choi, "Input feature selection for classification problems," IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 143-159, Jan. 2002. (Pubitemid 34236844)
24. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, min-redundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005. (Pubitemid 41245053)
25. K. Kira and L. A. Rendell, "The feature selection problem: Traditional methods and a new algorithm," in Proc. 10th Nat. Conf. AAAI, 1992, pp. 129-134.
26. J. E. Goin, "Classification bias of the k-nearest neighbor algorithm," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 3, pp. 379-381, May 1984.
27. J. M. Keller, M. R. Gray, and J. A. J. Givens, "Fuzzy k-nearest neighbor algorithm," IEEE Trans. Syst. Man Cybern., vol. SMC-15, no. 4, pp. 580-584, Jul./Aug. 1985.
28. J.-P. Vert, K. Tsuda, and B. Schölkopf, "A primer on kernel methods," in Kernel Methods in Computational Biology. Cambridge, MA, USA: MIT Press, 2004, pp. 35-70.
29. D. F. Specht, "Probabilistic neural networks," Neural Netw., vol. 3, no. 1, pp. 109-118, 1990.
30. R. Streit and T. Luginbuhl, "Maximum likelihood training of probabilistic neural networks," IEEE Trans. Neural Netw., vol. 5, no. 5, pp. 764-783, Sep. 1994.
31. G. P. Zhang, "Neural networks for classification: A survey," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 30, no. 4, pp. 451-462, Nov. 2000.
32. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Hoboken, NJ, USA: Wiley, 2001, ch. 4.
33. C. Y. Suen, C. Nadal, T. A. Mai, R. Legault, and L. Lam, "Recognition of totally unconstrained handwritten numerals based on the concept of multiple experts," in Proc. Int. Workshop Frontiers Handwriting Recogn., Apr. 1990, pp. 131-143.
34. C. Nadal, R. Legault, and C. Y. Suen, "Complementary algorithms for the recognition of totally unconstrained handwritten numerals," in Proc. 10th Int. Conf. Pattern Recogn., Jun. 1990, vol. A, pp. 434-449.
35. J. J. Hull, A. Commike, and T. K. Ho, "Multiple algorithms for handwritten character recognition," in Proc. Int. Workshop Frontiers Handwriting Recogn., Apr. 1990, pp. 117-129.
36. J. Sun, K. Huang, Y. Hotta, K. Fujimoto, and S. Naoi, "Degraded character recognition by complementary classifiers combination," in Proc. 9th ICDAR, Sep. 2007, vol. 2, pp. 579-583.
37. H. El-Shishini, M. Abdel-Mottaleb, M. El-Raey, and A. Shoukry, "A multistage algorithm for fast classification of patterns," Pattern Recognit. Lett., vol. 10, no. 4, pp. 211-215, Oct. 1989.
38. M. Fairhurst and H. A.Wahab, "An interactive two-level architecture for a memory network pattern classifier," Pattern Recognit. Lett., vol. 11, no. 8, pp. 537-540, Aug. 1990.
39. P. Pudil, J. Novovicova, S. Blaha, and J. Kittler, "Multistage pattern recognition with reject option," in Proc. 11th IAPR, Conf. B, Pattern Recogn. Method. Syst., 1992, vol. 2, pp. 92-95.
40. D. A. Denisov and A. K. Dudkin, "Model-based chromosome recognition via hypotheses construction/verification," Pattern Recognit. Lett., vol. 15, no. 3, pp. 299-307, Mar. 1994.
41. C. H. Tung, H. J. Lee, and J. Y. Tsai, "Multi-stage pre-candidate selection in handwritten Chinese character recognition systems," Pattern Recognit., vol. 27, no. 8, pp. 1093-1102, Aug. 1994.
42. J. Y. Zhou and T. Pavlidis, "Discrimination of characters by a multi-stage recognition process," Pattern Recognit., vol. 27, no. 11, pp. 1539-1549, Nov. 1994. (Pubitemid 124017643)
43. J. Franke and E. Mandler, "A comparison of two approaches for combining the votes of cooperating classifiers," in Proc. 11th IAPR, Conf. B, Pattern Recogn. Methodol. Syst., 1992, vol. 2, pp. 611-614.
44. F. Kimura and M. Shridhar, "Handwritten numerical recognition based on multiple algorithms," Pattern Recognit., vol. 24, no. 10, pp. 969-983, 1991.
45. S. Bagui and N. Pal, "A multistage generalization of the rank nearest neighbor classification rule," Pattern Recognit. Lett., vol. 16, no. 6, pp. 601-614, Jun. 1995.
46. T. Ho, J. Hull, and S. Srihari, "Decision combination in multiple classifier systems," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 1, pp. 66-75, Jan. 1994.
47. S. Hashem and B. Schmeiser, "Improving model accuracy using optimal linear combinations of trained neural networks," IEEE Trans. Neural Netw., vol. 6, no. 3, pp. 792-794, May 1995.
48. J. Kittler, M. Hatef, and R. P. W. Duin, "Combining classifiers," in Proc. 13th Int. Conf. Pattern Recogn., 1996, vol. 2, pp. 897-901.
49. B. Zhang and S. N. Srihari, "Class-wise multi-classifier combination based on Dempster-Shafer theory," in Proc. 7th ICARV, 2002, vol. 2, pp. 698-703.
50. G. Rogova, "Combining the results of several neural network classifiers," Neural Netw., vol. 7, no. 5, pp. 777-781, 1994.
51. L. Xu, A. Krzyzak, and C. Y. Suen, "Methods of combining multiple classifiers and their applications to handwriting recognition," IEEE Trans. Syst., Man, Cybern., vol. 22, no. 3, pp. 418-435, May/Jun. 1992.
52. P.-Y. Mignotte et al., "Adaptive fusion framework based on augmented reality training," IET Radar Sonar Navig., vol. 2, no. 2, pp. 146-154, Apr. 2008. (Pubitemid 351357131)
53. J. Fawcett et al., "Multiaspect classification of sidescan sonar images, four different approaches to fusing single-aspect information," IEEE J. Ocean. Eng., vol. 35, no. 4, pp. 863-876, Oct. 2010.
54. T. Fei, D. Kraus, and A. M. Zoubir, "A hybrid relevance measure for feature selection and its application to underwater objects recognition," in Proc. IEEE ICIP, 2012, pp. 97-100.
55. T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. Hoboken, NJ, USA: Wiley, 2006, ch. 2.
56. T. Fei, D. Kraus, and P. Berkel, "A new idea on feature selection and its application to the underwater object recognition," in Proc. 11th ECUA, 2012, pp. 52-59.
57. A. P. Dempster, "Upper and lower probabilities induced by a multivalued mapping," Ann. Math. Stat., vol. 38, no. 2, pp. 325-339, Apr. 1967.
58. G. Shafer, A Mathematical Theory of Evidence. Princeton, NJ, USA: Princeton Univ. Press, 1976.
59. L. Liu and R. R. Yager, "Classic works of the Dempster-Shafer theory of belief functions: An introduction," in Classic Works of the Dempster-Shafer Theory of Belief Functions. Berlin, Germany: Springer-Verlag, 2008, pp. 1-34.
60. T. Denoeux, "A k-nearest neighbor classification rule based on Dempster-Shafer theory," IEEE Trans. Syst., Man, Cybern., vol. 25, no. 5, pp. 804-813, May 1995.
61. P. Smets, "Constructing the pignistic probability function in a context of uncertainty," in Proc. 5th Annu. Conf. UAI, 1989, pp. 29-39.
62. P. Smets and R. Kennes, "The transferable belief model," Artif. Intell., vol. 66, no. 2, pp. 191-243, Apr. 1994.
63. C.W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
64. S. Canu, Y. Grandvalet, V. Guigue, and A. Rakotomamonjy, "SVM and kernel methods MATLAB toolbox," Perception Syst. Inf., INSA de Rouen, Rouen, France, 2005.
65. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. Hoboken, NJ, USA: Wiley, 2004, ch. 5.