Sparse Representation-Based ISAR Imaging Using Markov Random Fields

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 8, Issue 8, 2015, Pages 3941-3953

  Wang, Lu ^a   Zhao, Lifan ^a   Bi, Guoan ^a   Wan, Chunru ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b DSO NATIONAL LABORATORIES   (Singapore)

Author keywords

Continuity structure; inverse synthetic aperture radar (ISAR) imaging; Markov random fields (MRF); variational Bayes expectation maximization (VBEM)

Indexed keywords

ALGORITHMS; IMAGE SEGMENTATION; INVERSE PROBLEMS; INVERSE SYNTHETIC APERTURE RADAR; MARKOV PROCESSES; MAXIMUM PRINCIPLE; RADAR; RANDOM VARIABLES; STRUCTURAL FRAMES; SYNTHETIC APERTURE RADAR;

BAYESIAN FRAMEWORKS; EXPECTATION - MAXIMIZATIONS; INVERSE SYNTHETIC APERTURE RADAR (ISAR) IMAGING; MARKOV RANDOM FIELDS; MODEL PARAMETERS; SPARSE REPRESENTATION; STOPPING CRITERIA; VARIATIONAL BAYES;

RADAR IMAGING;

ALGORITHM; BAYESIAN ANALYSIS; MARKOV CHAIN; OPTIMIZATION; RADAR IMAGERY; SYNTHETIC APERTURE RADAR;

EID: 84941891466     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2359250     Document Type: Article

References (43)

1. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, Mar. 2001.
2. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc. vol. B, no. 58, pp. 267-288, 1996.
3. V. M. Patel, G. R. Easley, D. M. Healy, Jr., and R. Chellappa, "Compressed synthetic aperture radar," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 244-254, Apr. 2010.
4. L. Zhang et al., "Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3824-3838, Oct. 2010.
5. H. Wang, Y. Quan, M. Xing, and S. Zhang, "ISAR imaging via sparse probing frequencies," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 451-455, May 2011.
6. S. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41 ,no. 12, pp. 3397-3415, Dec. 1993.
7. G. Li, H. Zhang, X. Wang, and X. Xia, "ISAR 2-D imaging of uniformly rotating targets via matching pursuit," IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 2, pp. 1838-1846, Apr. 2012.
8. S. Ji, Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2346-2356, Jun. 2008.
9. G. Xu, M. Xing, L. Zhang, Y. Liu, and Y. Li, "Bayesian inverse synthetic aperture radar imaging," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 6, pp. 1150-1154, Nov. 2011.
10. J. Xu, Y. Pi, and Z. Cao, "Bayesian compressive sensing in synthetic aperture radar imaging," IET Radar Sonar Navig., vol. 6, no. 1, pp. 2-8, 2012.
11. M. Çetin and W. Karl, "Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization," IEEE Trans. Image Process., vol. 10, no. 4, pp. 623-631, Apr. 2001.
12. M. Tello Alonso, P. Lopez-Dekker, and J. J. Mallorqui, "A novel strategy for radar imaging based on compressive sensing," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 12, pp. 4285-4295, Dec. 2010.
13. N. Onhon and M. Çetin, "A Sparsity-driven approach for joint SAR imaging and phase error correction," IEEE Trans. Image Process., vol. 21, no. 4, pp. 2075-2088, Apr. 2012.
14. L. Zhao, L. Wang, G. Bi, and L. Yang, "An autofocus technique for high-resolution inverse synthetic aperture radar imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6392-6403, Oct. 2014.
15. L. Wang, L. Zhao, G. Bi, C. Wan, and L. Yang, "Enhanced ISAR imaging by exploiting the continuity of the target scene," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 9, pp. 5736-5750, Sep. 2014.
16. J. Zhang, "The mean field theory in EM procedures for Markov random fields," IEEE Trans. Signal Process., vol. 40, no. 10, pp. 2570-2583, Oct. 1992.
17. D. Gleich, "Markov random field models for non-quadratic regularization of complex SAR images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 3, pp. 952-961, Jun. 2012.
18. R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Trans. Inf. Theory, vol. 56, no. 4, pp. 1982-2001, Apr. 2010.
19. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
20. D. Donoho, M. Elad, and V. Temlyakov, "Stable recovery of sparse over-complete representations in the presence of noise," IEEE Trans. Inf. Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.
21. A. Dremeau, C. Herzet, and L. Daudet, "Boltzmann machine and meanfield approximation for structured sparse decompositions," IEEE Trans. Signal Process., vol. 60, no. 7, pp. 3425-3438, Jul. 2012.
22. T. Peleg, Y. C. Eldar, and M. Elad, "Exploiting statistical dependencies in sparse representations for signal recovery," IEEE Trans. Signal Process., vol. 60, no. 5, pp. 2286-2303, May 2012.
23. P. J. Garrigues and B. A. Olshausen, "Learning horizontal connections in a sparse coding model of natural images," in Advances in Neural Information Processing System, vol. 20, J. C. Platt, D. Koller, Y. Singer, and S. Roweis, Eds. Cambridge, U.K.: MIT Press, 2008, pp. 505-512.
24. V. Cevher, M. F. Duarte, C. Hegde, and R. G. Baraniuk, "Sparse signal recovery using Markov random fields," in Proc. Workshop Neural Inform. Proc. Sys. (NIPS), Vancouver, BC, Canada, Dec. 2008.
25. S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Bayesian compressive sensing using laplace priors," IEEE Trans. Image Process., vol. 19, no. 1, pp. 53-63, Jan. 2010.
26. M. J. Beal, "Variational algorithms for approximate Bayesian inference," Ph.D. dissertation, Gatsby Computational Neuroscience Unit, Univ. College London, London, U.K., 2003.
27. D. G. Tzikas, A. C. Likas, and N. P. Galatsanos, "The variational approximation for Bayesian inference," IEEE Signal Process. Mag., vol. 25, no. 6, pp. 131-146, Nov. 2008.
28. Z. Zhang and B. D. Rao, "Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation," IEEE Trans. Signal Process., vol. 61, no. 8, pp. 2009-2015, Apr. 2013.
29. M. Zhou et al., "Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images," IEEE Trans. Image Process., vol. 57, no. 9, pp. 3488-3497, Sep. 2009.
30. L. He, H. Chen, and L. Carin, "Tree-structured compressive sensing with variational Bayesian analysis," IEEE Signal Process. Lett., vol. 17, no. 3, pp. 233-236, Mar. 2010.
31. L. He and L. Carin, "Exploiting structure in wavelet-based Bayesian compressive sensing," IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3488-3497, Sep. 2009.
32. C. La and M. N. Do, "Tree-based orthogonal matching pursuit algorithm for signal reconstruction," in IEEE Int. Conf. Image Process. (ICIP), Atlanta, GA, USA, Oct. 2006, pp. 1277-1280.
33. Y. C. Eldar, P. Kuppinger, and H. Bolcskei, "Block-sparse signals: Uncertainty relations and efficient recovery," IEEE Trans. Signal Process., vol. 58, no. 6, pp. 3042-3054, 2010.
34. X. Lv, G. Bi, and C. Wan, "The group lasso for stable recovery of blocksparse signal representations," IEEE Trans. Signal Process., vol. 59, no. 4, pp. 1371-1382, Apr. 2011.
35. D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk. (2009). Distributed Compressed Sensing [Online]. Available: http://arxiv.org/pdf/0901.3403v1.pdf
36. L. Yu, H. Sun, J. P. Barbot, and G. Zheng, "Bayesian compressive sensing for cluster structured sparse signals," Signal Process., vol. 92, no. 1, pp. 259-269, 2012.
37. L. Yu, J. P. Barbot, G. Zheng, and H. Sun. (2011). Compressive Sensing for Cluster Structured Sparse Signals: Variational Bayes Approach [Online]. Available: https://sites.google.com/site/link2yulei/research
38. N. Dobigeon, A. Basarab, D. Kouame, and J. Tourneret, "Regularized Bayesian compressed sensing in ultrasound imaging," in Proc. IEEE 20th Eur. Signal Process. Conf. (EUSIPCO), Bucharest, Romania, Aug. 2012, pp. 2600-2604.
39. C. Quinsac, N. Dobigeon, A. Basarab, J. Y. Tourneret, and D. Kouame, "Bayesian compressed sensing in ultrasound imaging," in Proc. IEEE Int. Workshop Comput. Adv. Multi-Sensor Adaptive Process. (CAMSAP),San Juan, Puerto Rico, Dec. 2011, pp. 101-104.
40. S. Chatterjee, D. Sundman, M. Vehkapera, and M. Skoglund, "Projection-based and look-ahead strategies for atom selection," IEEE Trans. Signal Process., vol. 60, no. 2, pp. 634-647, Feb. 2012.
41. J. L. Walker, "Range-Doppler imaging of rotating objects," IEEE Trans. Aerosp. Electron. Syst., vol. 16, no. 1, pp. 23-52, Jan. 1980.
42. C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed. New York, NY, USA: Springer, 2004.
43. D. P. Wipf and B. D. Rao, "Sparse Bayesian learning for basis selection," IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2153-2164, Aug. 2004.