volume | PIER Journals

Abstract

The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features are extracted as a l₁-norm sparse problem on the basis of the SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed of training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experimental results illustrate the effectiveness and robustness of the proposed approach.

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.
doi:10.2528/PIER11112110 Google Scholar

2. Chen, J., J. Gao, Y. Zhu, W. Yang, and P. Wang, "A novel image formation algorithm for high-resolution wide-swath spaceborne SAR using compressed sensing on azimuth displacement phase center antenna," Progress In Electromagnetics Research, Vol. 125, 527-543, 2012.
doi:10.2528/PIER11121101 Google Scholar

3. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011. Google Scholar

4. Chiang, C.-Y., Y.-L. Chang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 11, 35-57, 2011.
doi:10.2528/PIER11061507 Google Scholar

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

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. Google Scholar

7. Papson, S. and R.-M. Narayanan, "Classification via the shadow region in SAR imagery," IEEE Trans. on Aerospace and Electronic Systems, Vol. 48, 969-980, 2012.
doi:10.1109/TAES.2012.6178042 Google Scholar

8. Huang, C.-W. and K.-C. Lee, "Application of ICA technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 105, 157-170, 2010.
doi:10.2528/PIER10042305 Google Scholar

9. Lee, K.-C., J.-S. Ou, and M.-C. Fang, "Application of SVD noise-reduction technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 81, 447-459, 2008.
doi:10.2528/PIER08032101 Google Scholar

10. Runkle, P., L.-H. Nguyen, J.-H. McClellan, and L. Carin, "Multi-aspect target detection for SAR imagery using hidden Markov models," IEEE Trans. on Geoscience and Remote Sensing, Vol. 39, 46-55, 2001.
doi:10.1109/36.898664 Google Scholar

11. 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, 1230-1242, 2002.
doi:10.1109/TAES.2002.1145746 Google Scholar

12. 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.
doi:10.2528/PIER10071601 Google Scholar

13. Potter, L.-C. and R.-L. Moses, "Attributed scattering centers for SAR ATR," IEEE Trans. on Image Processing, Vol. 6, 79-91, 1997.
doi:10.1109/83.552098 Google Scholar

14. Gerry, M.-J., L.-C. Potter, I.-J. Gupta, and A.-V. Merwe, "A parametric model for synthetic aperture radar measurements," IEEE Trans. on Antennas and Propagation, Vol. 47, 1179-1188, 1999.
doi:10.1109/8.785750 Google Scholar

15. Park, S.-H., S.-H., J.-H. Lee, and K.-T. Kim, "Performance analysis of the scenario-based construction method for real target ISAR recognition," Progress In Electromagnetics Research, Vol. 128, 137-151, 2012. Google Scholar

16. Zhao, Q. and J.-C. Principe, "Support vector machines for SAR automatic target recognition," IEEE Trans. on Aerospace and Electronic Systems, Vol. 37, 643-654, 2001.
doi:10.1109/7.937475 Google Scholar

17. 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.
doi:10.2528/PIER07012903 Google Scholar

18. Zhang, Y. and L.Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012. Google Scholar

19. 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.
doi:10.2528/PIER08041803 Google Scholar

20. Wu, Y., Z.-X. Tang, B. Zhang, and Y. Xu, "Permeability measurement of ferromagnetic materials in microwave frequency range using support vector machine regression," Progress In Electromagnetics Research, Vol. 70, 247-256, 2007.
doi:10.2528/PIER07012801 Google Scholar

21. Candès, E.-J. and M.-B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, 21-30, 2008.
doi:10.1109/MSP.2007.914731 Google Scholar

22. Candès, E.-J. and T. Tao, "Decoding by linear programming," IEEE Trans. on Information Theory, Vol. 51, 4203-4215, 2005.
doi:10.1109/TIT.2005.858979 Google Scholar

23. Donoho, D.-L., "Compressed sensing," IEEE Trans. on Information Theory, Vol. 52, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582 Google Scholar

24. Wei, S.-J., X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011. Google Scholar

25. 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, 210-227, 2009.
doi:10.1109/TPAMI.2008.79 Google Scholar

26. Zhang, S., X. Zhao, and B. Lei, "Robust facial expression recognition via compressive sensing," Sensors, Vol. 12, 3747-3761, 2012.
doi:10.3390/s120303747 Google Scholar

27. 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, 2481-2497, 2012.
doi:10.1109/TAES.2012.6237604 Google Scholar

28. Ji, S., Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. on Signal Processing, Vol. 56, 2346-2356, 2008.
doi:10.1109/TSP.2007.914345 Google Scholar

29. Potter, L.-C., E. Ertin, J.-T. Parker, and M. Çetin, "Sparsity and compressed sensing in radar imaging," Proceedings of the IEEE, Vol. 98, 1006-1020, 2010.
doi:10.1109/JPROC.2009.2037526 Google Scholar

30. Zhou, J., Z. Shi, X. Cheng, and Q. Fu, "Automatic target recognition of SAR images based on global scattering center model," IEEE Trans. on Geoscience and Remote Sensing, Vol. 49, No. 10, 3713-3729, 2011.
doi:10.1109/TGRS.2011.2162526 Google Scholar

31. Çetin, M. and W.-C. Karl, "Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization," IEEE Trans. on Image Processing, Vol. 10, 623-631, 2001.
doi:10.1109/83.913596 Google Scholar

32. Chen, S.-S., D.-L. Donoho, and M.-A. Saunders, "Atomic decomposition by basis pursuit," SIAM Review, 129-159, 2001.
doi:10.1137/S003614450037906X Google Scholar

33. Tibshirani, R., "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society. Series B (Methodological), Vol. 58, 267-288, 1996. Google Scholar

34. Tipping, M.-E., "Sparse Bayesian learning and the relevance vector machine," Journal of Machine Learning Research, Vol. 1, 211-244, 2001. Google Scholar

35. Xu, J., Y. Pi, and Z. Cao, "Bayesian compressive sensing in synthetic aperture radar imaging," IET Radar, Sonar & Navigation, Vol. 6, 2-8, 2012.
doi:10.1049/iet-rsn.2010.0375 Google Scholar

36. 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, 1230-1244, 2000.
doi:10.1117/1.602495 Google Scholar

Dr. Xinzheng Zhang

Dr. Jianhong Qin

Dr. Guojun Li