Progress In Electromagnetics Research B
ISSN: 1937-6472
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 60 > pp. 63-77


By X. Zhang, Q. Wu, S. Liu, J. Qin, and W. Song

Full Article PDF (560 KB)

A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

X. Zhang, Q. Wu, S. Liu, J. Qin, and W. Song, "Hrr Profiles Time-Frequency Non-Negative Sparse Coding for SAR Target Classification," Progress In Electromagnetics Research B, Vol. 60, 63-77, 2014.

1. An, D. X., Z.-M. Zhou, X.-T. Huang, and T. Jin, "A novel imaging approach for high resolution squinted spotlight SAR based on the deramping-based technique and azimuth nlcs principle," Progress In Electromagnetics Research, Vol. 123, 485-508, 2012.

2. Chiang, C.-Y., Y.-L. Chang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 119, 35-57, 2011.

3. Dudgeon, D.-E. and R.-T. Lacoss, "An overview of automatic target recognition," The Lincoln Laboratory Journal, Vol. 6, 3-9, 1993.

4. Zhao, Q. and J.-C. Principe, "Support vector machines for SAR automatic target recognition," IEEE Trans. on Aerospace and Electronic Systems, Vol. 37, No. 2, 643-654, 2001.

5. Zhao, Q., J. C. Principe, V. L. Brennan, D. Xu, and Z. Wang, "Synthetic aperture radar automatic target recognition with three strategies of learning and representation," Optical Engineering, Vol. 39, 1230-1236, 2000.

6. Huan, R.-H. and Y. Pan, "Target recognition for multi-aspect SAR images with fusion strategies," Progress In Electromagnetics Research, Vol. 134, 267-288, 2013.

7. Papson , S. and R.-M. Narayanan, "Classification via the shadow region in SAR imagery," IEEE Trans. on Aerospace and Electronic Systems, Vol. 48, No. 2, 969-980, 2012.

8. Potter, L.-C. and R.-L. Moses, "Attributed scattering centers for SAR ATR," IEEE Trans. on Image Processing, Vol. 6, No. 1, 79-91, 1997.

9. Liao, X.-J., P. Runkle, and L. Carin, "Identification of ground targets from sequential high-range-resolution radar signatures," IEEE Trans. on Aerospace and Electronic Systems, Vol. 38, No. 4, 1230-1242, 2002.

10. Wong, S., "High range resolution profiles as motion-invariant features for moving ground targets identification in SAR-based automatic target recognition," IEEE Trans. on Aerospace and Electronic Systems, Vol. 45, No. 3, 1017-1039, 2009.

11. Albrecht, T. W. and S. C. Gustafson, "Hidden Markov models for classifying SAR target images," Proceedings of SPIE, Algorithms for Synthetic Aperture Radar Imagery XI, Vol. 5427, Orlando, FL, USA, Apr. 2004..

12. Nishimoto, M., X. Liao, and L. Carin, "Target identification from multi-aspect high range-resolution radar signatures using a hidden Markov model," IEICE Trans. Electronics, Vol. 87, 1706-1714, 2004.

13. Han, S.-K., H.-T. Kim, S.-H. Park, and K.-T. Kim, "Efficient radar target recognition using a combination of range profile and time-frequency analysis," Progress In Electromagnetics Research, Vol. 108, 131-140, 2010.

14. Kim, K. T., I. S. Choi, and H. T. Kim, "Efficient radar target classification using adaptive joint time-frequency processing," IEEE Trans. on Antennas and Propagation, Vol. 2, No. 48, 1789-1801, 2000.

15. Thayaparan, T., P. Suresh, S. Qian, K. Venkataramaniah, S. SivaSankaraSai, and K. Sridharan, "Micro-Doppler analysis of a rotating target in synthetic aperture radar," IET Signal Processing, Vol. 4, 245-255, 2010.

16. Olshausen, B. A., "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, Vol. 381, 607-609, 1996.

17. Wright, J., A.-Y. Yang, A. Ganesh, S.-S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, 210-227, 2009.

18. Zhang, H., N.-M. Nasrabadi, Y. Zhang, and T.-S. Huang, "Multi-view automatic target recognition using joint sparse representation," IEEE Trans. on Aerospace and Electronic Systems, Vol. 48, No. 3, 2481-2497, 2012.

19. Liu, H., C. Liu, and Y. Huang, "Adaptive feature extraction using sparse coding for machinery fault diagnosis," Mechanical Systems and Signal Processing,, Vol. 25, 558-574, 2011.

20. Murray, J. F. and K. Kreutz-Delgado, "Learning sparse overcomplete codes for images," The Journal of VLSI Signal Processing, Vol. 45, 97-110, 2006.

21. Wang, Y., Q. Song, T. Jin, Y. Shi, and X.-T. Huang, "Sparse time-frequency representation based feature extraction method for landmine discrimination," Progress In Electromagnetics Research, Vol. 133, 459-475, 2013.

22. Hoyer, P. O., "Modeling receptive fields with non-negative sparse coding," Neurocomputing, Vol. 52, 547-552, 2003.

23. Schmidt, M. N., J. Larsen, and F. T. Hsiao, "Wind noise reduction using non-negative sparse coding," Proceedings of IEEE Workshop on Machine Learning for Signal Processing, 431-436, 2007.

24. Tan, C.-P., J.-Y. Koay, K.-S. Lim, H.-T. Ewe, and H.-T. Chuah, "Classification of multi-temporal SAR images for rice crops using combined entropy decomposition and support vector machine technique," Progress In Electromagnetics Research, Vol. 71, 19-39, 2007.

25. Zhang, Y., S.Wang, and Z. Dong, "Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree," Progress In Electromagnetics Research, Vol. 144, 171-184, 2014.

26. Angiulli, G., D. De Carlo, G. Amendola, E. Arnieri, and S. Costanzo, "Support vector regression machines to evaluate resonant frequency of elliptic substrate integrate waveguide resonators," Progress In Electromagnetics Research, Vol. 83, 107-118, 2008.

27. Ross, T. D., S. W. Worrell, V. J. Velten, J. C. Mossing, and M. L. Bryant, "Standard SAR ATR evaluation experiments using the MSTAR public release data set," Proceedings of SPIE, Algorithms for Synthetic Aperture Radar Imagery V, Vol. 3370, 566-570, 1998.

28. Guillamet, D., B. Schiele, and J. Vitria, "Analyzing non-negative matrix factorization for image classification," Proceedings of 16th International Conference on Pattern Recognition, 116-119, 2002.

© Copyright 2010 EMW Publishing. All Rights Reserved