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2016-04-15
Tomography SAR Imaging Strategy Based on Block-Sparse Model
By
Progress In Electromagnetics Research M, Vol. 47, 191-200, 2016
Abstract
The compressed sensing (CS) based imaging methods for tomography SAR perform well in the case of large number of baselines. Unfortunately, for the current tomography SAR, the baselines are obtained from many multi-pass acquisitions on the same scene, which is expensive and can be severely affected by temporal decorrelation. In order to reduce the number of baselines, a novel strategy for tomography SAR imaging by introducing the block-sparsity theory into the imaging processing is proposed in this paper. Using neighboring pixels information in reconstruction, the proposed method can overcome the imaging quality limitation imposed by the low number of baselines. The results with simulation data under the additive gaussian noise case are presented to verify the effectiveness of the proposed method.
Citation
Xiao-Zhen Ren Fuyan Sun , "Tomography SAR Imaging Strategy Based on Block-Sparse Model," Progress In Electromagnetics Research M, Vol. 47, 191-200, 2016.
doi:10.2528/PIERM16010904
http://www.jpier.org/PIERM/pier.php?paper=16010904
References

1. Curlander, J. C. and R. N. Mcdonough, Synthetic Aperture Radar: System and Signal Processing, John Wiley & Sons, 1991.

2. Floyd, H. and A. J. Lewis, Principles and Applications of Imaging Radar - Manual of Remote Sensing, Wiley, New York, 1998.

3. Morrison, K., J. C. Bennett, and M. Nolan, "Using DInSAR to separate surface and subsurface features," IEEE Transaction on Geoscience and Remote Sensing, Vol. 51, 3424-3430, 2013.
doi:10.1109/TGRS.2012.2226183

4. Barrett, B., P. Whelan, and E. Dwyer, "Detecting changes in surface soil moisture content using differential SAR interferometry," International Journal of Remote Sensing, Vol. 34, 7091-7112, 2013.
doi:10.1080/01431161.2013.813654

5. Reigber, A. and A. Moreira, "First demonstration of airborne SAR tomography using multibaseline L-band data," IEEE Transaction on Geoscience and Remote Sensing, Vol. 38, 2142-2150, 2000.
doi:10.1109/36.868873

6. Reigber, A., F. Lombardini, F. Viviani, M. Nannini, and A. Martinez del Hoyo, "Three-dimensional and higher-order imaging with tomographic SAR: Techniques, applications, issues," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2915-2918, 2015.

7. Lombardini, F. and A. Reigber, "Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data," IEEE International Geoscience and Remote Sensing Symposium 2003, 2014-2016, Toulouse, France, 2003.

8. Lombardini, F., F. Cai, and D. Pasculli, "Spaceborne 3-D SAR tomography for analyzing garbled urban scenarios: Single-look superresolution advances and experiments," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, 960-968, 2013.
doi:10.1109/JSTARS.2012.2211339

9. Sauer, S., L. Ferro-Famil, A. Reigber, and E. Pottier, "Three-dimensional imaging and scattering mechanism estimation over urban scenes using dual-baseline polarimetric InSAR observations at L-band," IEEE Transaction on Geoscience and Remote Sensing, Vol. 49, 4616-4629, 2011.
doi:10.1109/TGRS.2011.2147321

10. Lombardini, F., M. Pardini, and F. Gini, "Sector interpolation for 3D SAR imaging with baseline diversity data," IEEE 2007 Waveform Diversity and Design Conference, 297-301, Pisa, Italy, 2007.

11. Lombardini, F. and M. Pardini, "First experiment of sector interpolated SAR tomography," IEEE International Geoscience and Remote Sensing Symposium, 21-24, 2010.

12. Fornaro, G., F. Serafino, and F. Soldovieri, "Three-dimensional focusing with multipass SAR data," IEEE Transaction on Geoscience and Remote Sensing, Vol. 41, 507-517, 2003.
doi:10.1109/TGRS.2003.809934

13. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transaction on Information Theory, Vol. 52, 489-509, 2006.
doi:10.1109/TIT.2005.862083

14. Donoho, D., "Compressed sensing," IEEE Transaction on Information Theory, Vol. 52, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582

15. Chen, W. and I. J. Wassell, "Optimized node selection for compressive sleeping wireless sensor networks," IEEE Transactions on Vehicular Technology, Vol. 65, 827-836, 2016.
doi:10.1109/TVT.2015.2400635

16. Zhang, Y., S. Wang, G. Ji, and Z. Dong, "Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging," IEEJ Transactions on Electrical and Electronic Engineering, Vol. 10, 116-117, 2015.
doi:10.1002/tee.22059

17. Zhang, Y., Z. Dong, P. Phillips, S. Wang, G. Ji, and J. Yang, "Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging," Information Sciences, Vol. 322, 115-132, 2015.
doi:10.1016/j.ins.2015.06.017

18. Zhu, X. X. and R. Bamler, "Tomographic SAR inversion by L1-norm regularization - The compressive sensing approach," IEEE Transaction on Geoscience and Remote Sensing, Vol. 48, 3839-3846, 2013.
doi:10.1109/TGRS.2010.2048117

19. Budillon, A., A. Evangelista, and G. Schirinzi, "Three-dimensional SAR focusing from multipass signals using compressive sampling," IEEE Transaction on Geoscience and Remote Sensing, Vol. 49, 488-499, 2011.
doi:10.1109/TGRS.2010.2054099

20. Schmitt, M. and U. Stilla, "Compressive sensing based layover separation in airborne single-pass multi-baseline InSAR data," IEEE Geoscience and Remote Sensing Letters, Vol. 10, 313-317, 2013.
doi:10.1109/LGRS.2012.2204230

21. Eldar, Y. C. and M. Mishali, "Robust recovery of signals from a structured union of subspaces," IEEE Transaction on Information Theory, Vol. 55, 5302-5316, 2009.
doi:10.1109/TIT.2009.2030471

22. Eldar, Y. C. and H. Bölcskei, "Block-sparsity: Coherence and efficient recovery," IEEE International Conference on Acoustics, Speech and Signal Processing, 2885-2888, 2009.

23. Eldar, Y. C., P. Kuppinger, and H. Bolcskei, "Block-sparse signals: Uncertainty relations and efficient recovery," IEEE Transaction on Signal Processing, Vol. 58, 3042-3054, 2010.
doi:10.1109/TSP.2010.2044837

24. Aguilera, E., M. Nannini, and A. Reigber, "Multisignal compressed sensing for polarimetric SAR tomography," IEEE Geoscience and Remote Sensing Letters, Vol. 9, 871-875, 2012.
doi:10.1109/LGRS.2012.2185482

25. Fishler, E. and H. Messer, "Detection of signals by information theoretic criteria: General asymptotic performance analysis," IEEE Transactions on Signal Processing, Vol. 50, 1027-1036, 2002.
doi:10.1109/78.995060

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