Detection of buried targets via active selection of labeled data: Application to sensing subsurface UXO

IEEE Transactions on Geoscience and Remote Sensing

Volumn 42, Issue 11, 2004, Pages 2535-2543

  Zhang, Yan ^a   Liao, Xuejun ^a   Carin, Lawrence ^a  

^a Duke University   (United States)

Author keywords

Active learning; Fisher information; Kernel matching pursuit; Squared error; Subsurface sensing; Unexploded ordnance (UXO)

Indexed keywords

ALGORITHMS; FUNCTIONS; MAGNETOMETERS; MATRIX ALGEBRA; SENSORS; SET THEORY; VECTORS;

ACTIVE LEARNING; ELECTROMAGNETIC INDUCTION; FISHER INFORMTION; KERNEL MATCHING PURSUIT; SQUARED ERRORS; SUBSURFACE SENSING; UNEXPLODED ORDINANCE (UXO);

TARGETS;

EID: 9944254253     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2004.836270     Document Type: Article

References (18)

1. C. T. Schroder, W. R. Scott, and G. D. Larson, "Elastic waves interacting with buried land mines: A study using the FDTD method," IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 1405-1415, June 2002.
2. B. Barrow and H. H. Nelson, "Model-based characterization of electromagnetic induction signatures obtained with the MTADS electromagnetic array," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 1279-1285, June 2001.
3. H. H. Nelson and J. R. McDonald, "Multisensor towed array detection system for UXO detection," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 1139-1145, June 2001.
4. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
5. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
6. C. J. C. Burges, "A Tutorial on support vector machines for pattern recognition," Data Mining Knowl. Discov., vol. 2, no. 2, pp. 121-167, 1998.
7. M. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
8. P. Vincent and Y. Bengio, "Kernel matching pursuit," Mach. Learn., vol. 48, pp. 165-187, 2002.
9. B. Schölkopf, K. K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, "Comparing support vector machines with Gaussian kernels to radial basis function classifiers," IEEE Trans. Signal Processing, vol. 45, pp. 2758-2765, Nov. 1997.
10. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 2000.
11. B. Schölkopf and A. Smola, Learning with Kernels, Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2002.
12. V. V. Fedorov, Theory of Optimal Experiments. New York: Academic, 1972.
13. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
14. M. Stone, "Cross-validatory choice and assessment of statistical predictions," J. R. Statist Soc., ser. B, vol. 36, pp. 111-147, 1974.
15. I. J. Won, D. A. Keiswetter, and D. R. Hanson, "GEM-3: A monostatic broadband electromagnetic induction sensor," J. Environ. Eng. Geophys., vol. 2, pp. 53-64, Mar. 1997.
16. N. Geng, C. E. Baum, and L. Carin, "On the low-frequency natural response of conducting and permeable targets," IEEE Trans. Geosci. Remote Sensing, vol. 37, pp. 347-359, Jan. 1999.
17. L. Carin, H. Yu, Y. Dalichaouch, A. R. Perry, P. V. Czipott, and C. E. Baum, "On the wideband EMI response of a rotationally symmetric permeable and conducting target," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 1206-1213, June 2001.
18. Y. Zhang, L. M. Collins, H. Yu, C. E. Baum, and L. Carin, "Sensing of unexploded ordnance with magnetometer and induction data: Theory and signal processing," IEEE Trans. Geosci. Remote Sensing, vol. 41, SP. 1005-1015, May 2003.