Open Access

Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction


Cite

1. Nicolas, J.-M., G. Vasile, M. Gay, F. Tupin, Em. Trouvé. SAR Processing in the Temporal Domain: Application to Direct Interferogram Generation and Mountain Glacier Monitoring Can. – J. Remote Sensing, Vol. 33, 2007, No 1, pp. 52-59.10.5589/m07-005Search in Google Scholar

2. Leijen, V., F. R. Hanssen. Interferometric Radar Meteorology: Resolving the Acquisition Ambiguity. – In: CEOS SAR Workshop, Ulm Germany, 27-28 May 2004, pp. 6-14.Search in Google Scholar

3. Colesanti, C., A. Ferretti, F. Novali, C. Prati, F. Rocca. SAR Monitoring of Progressive and Seasonal Ground Deformation Using the Permanent Scatterers Technique. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, July 2003, No 7, pp. 1685-1701.10.1109/TGRS.2003.813278Search in Google Scholar

4. Figueiredo, M. A. T., R. D. Nowak, S. J. Wright. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems. – IEEE Journal of Selected Topics in Signal Processing, Vol. 1, December 2007, No 4, pp. 586-597.10.1109/JSTSP.2007.910281Search in Google Scholar

5. Tropp, J. Just Relax: Convex Programming Methods for Identifying Sparse Signals. – IEEE Transactions on Information Theory, Vol. 51, 2006, pp. 1030-1051.10.1109/TIT.2005.864420Search in Google Scholar

6. Kim, S.-J., K. Koh, M. Lustig, S. Boyd, D. Gorinevsky. An Interior-Point Method for Large-Scale l1-Regularized Least Squares. – IEEE Journal of Selected Topics in Signal Processing, Vol. 1, 4 December 2007, pp. 606-617.10.1109/JSTSP.2007.910971Search in Google Scholar

7. Donaho, D. L. Compressed Sensing. – IEEE Trans. on Inf. Theory, Vol. 52, 2006, No 4, pp.1289-1306.10.1109/TIT.2006.871582Search in Google Scholar

8. Hayashi, K., M. Nagahara, T. Tanaka. A User’s Guide to Compressed Sensing for Communication Systems. – IEICE Trans. on Communications, Vol. E96-B, March 2013, No 3, pp. 685-712.10.1587/transcom.E96.B.685Search in Google Scholar

9. Gurbuza, A. C., J. H. McClellanb, W. R. Scott, B. Jr. Compressive Sensing for Subsurface Imaging Using Ground Penetrating Radar. – Signal Processing, Vol. 89, October 2009, No 10, pp. 1959-1972.10.1016/j.sigpro.2009.03.030Search in Google Scholar

10. Cai, J.-L., C.-M. Tong, W.-J. Zhong, W.-J. Ji. 3D Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing. – Progress in Electromagnetics Research M, Vol. 23, 2012, pp. 153-165.10.2528/PIERM11121206Search in Google Scholar

11. McClellan, C. J. H., W. R. Scott. A Compressive Sensing Data Acquisition and Imaging Method for Stepped-Frequency GPRs. – IEEE Transation on Signal Processing, Vol. 57, July 2009, No 7, pp. 2640-2650.10.1109/TSP.2009.2016270Search in Google Scholar

12. Shastry, M. C., R. M. Narayanan, M. Rangaswamy. Analysis of the Tolerance of Compressive Noise Radar Systems to Multiplicative Perturbations. – In: Proc. of SPIE’9109, Compressive Sensing III, 910905, 23 May 2014.10.1117/12.2053116Search in Google Scholar

13. Lin, Y. G., B. C. Zhang, W. Hong, Y. R. Wu. Along-Track Interferometric SAR Imaging Based on Distributed Compressed Sensing. – Electronics Letters, Vol. 46, 10 June 2010, No 12, p. 858-860.10.1049/el.2010.0710Search in Google Scholar

14. Yang, J., J. Thompson, X. Huang, T. Jin. Random-Frequency SAR Imaging Based on Compressed Sensing. – IEEE Trans. on Geoscience and Remote Sensing, Vol. 51, February 2013, No 2, pp. 983-994.10.1109/TGRS.2012.2204891Search in Google Scholar

15. Li, J., S. Zhang, J. Chang. Applications of Compressed Sensing for Multiple Transmitters Multiple Azimuth Beams SAR Imaging. – Progress in Electromagnetics Research, Vol. 127, 2012, pp. 259-275.10.2528/PIER12021307Search in Google Scholar

16. Wei, S.-J., X.-L. Zhang, J. Shi. Linear Array SAR Imaging Via Compressed Sensing. – Progress in Electromagnetics Research, Vol. 117, 2011, pp. 299-319.10.2528/PIER11033105Search in Google Scholar

17. Wei, S.-J., X.-L. Zhang, J. Shi, G. Xiang. Sparse Reconstruction for SAR Imaging Based on Compressed Sensing. – Progress in Electromagnetics Research, Vol. 109, 2010, pp. 63-81.10.2528/PIER10080805Search in Google Scholar

18. Qiu, W., E. Giusti, A. Bacci, M. Martorella, F. Berizzi et al. Compressive Sensing for Passive ISAR with DVB-T Signal. – In: Proc. of IRS-2013, 19-21 June 2013, pp. 113-118.Search in Google Scholar

19. Kim, S., K. Koh, M. Lustig, S. Boyd, D. Gorinvesky. A Method for Large-Scale ℓ1-Regularized Least Squares Problems with Applications in Signal Processing and Statistics. Tech. Report, Dept. of Electrical Engineering, Stanford University, 2007. www.stanford.edu/~boyd/l1_ls.html10.1109/JSTSP.2007.910971Search in Google Scholar

eISSN:
1314-4081
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology