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.
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.
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.
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.
5. Dudgeon, D.-E. and R.-T. Lacoss, "An overview of automatic target recognition," The Lincoln Laboratory Journal, Vol. 6, 3-9, 1993.
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, 969-980, 2012.
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.
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.
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.
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.
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.
13. Potter, L.-C. and R.-L. Moses, "Attributed scattering centers for SAR ATR," IEEE Trans. on Image Processing, Vol. 6, 79-91, 1997.
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.
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.
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.
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.
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.
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.
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.
21. Candès, E.-J. and M.-B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, 21-30, 2008.
22. Candès, E.-J. and T. Tao, "Decoding by linear programming," IEEE Trans. on Information Theory, Vol. 51, 4203-4215, 2005.
23. Donoho, D.-L., "Compressed sensing," IEEE Trans. on Information Theory, Vol. 52, 1289-1306, 2006.
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.
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.
26. Zhang, S., X. Zhao, and B. Lei, "Robust facial expression recognition via compressive sensing," Sensors, Vol. 12, 3747-3761, 2012.
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.
28. Ji, S., Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. on Signal Processing, Vol. 56, 2346-2356, 2008.
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.
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.
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.
32. Chen, S.-S., D.-L. Donoho, and M.-A. Saunders, "Atomic decomposition by basis pursuit," SIAM Review, 129-159, 2001.
33. Tibshirani, R., "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society. Series B (Methodological), Vol. 58, 267-288, 1996.
34. Tipping, M.-E., "Sparse Bayesian learning and the relevance vector machine," Journal of Machine Learning Research, Vol. 1, 211-244, 2001.
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.
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.