Baseline distribution optimization and missing data completion in wavelet-based CS-TomoSAR

Research Paper
  • 113 Downloads

Abstract

In this paper, we propose a coherence of measurement matrix-based baseline distribution optimization criterion, together with an L 1 regularization missing data completion method for unobserved baselines (not belonging to the actual baseline distribution), to facilitate wavelet-based compressive sensingtomographic synthetic aperture radar imaging (CS-TomoSAR) in forested areas. Using M actual baselines, we first estimate the optimal baseline distribution with N baselines (N > M), including NM unobserved baselines, via the proposed coherence criterion. We then use the geometric relationship between the actual and unobserved baseline distributions to reconstruct the transformation matrix by solving an L 1 regularization problem, and calculate the unobserved baseline data using the measurements of actual baselines and the estimated transformation matrix. Finally, we exploit the wavelet-based CS technique to reconstruct the elevation via the completed data of N baselines. Compared to results obtained using only the data of actual baselines, the recovered image based on the dataset obtained by our proposed method shows higher elevation recovery accuracy and better super-resolution ability. Experimental results based on simulated and real data validated the effectiveness of the proposed method.

Keywords

tomographic synthetic aperture radar imaging (TomoSAR) compressive sensing (CS) baseline distribution optimization coherence of measurement matrix 

Notes

Acknowledgements

This work was supported by Chinese Academy of Sciences/State Administration of Foreign Experts Affairs International Partnership Program Creative Research Team and National Natural Science Foundation of China (Grant No. 61571419). The authors would like to thank Dragon 3 Project (ID10609) and Prof. Erxue Chen and Prof. Stefano Tebaldini for providing the Biomass dataset.

References

  1. 1.
    Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans Geosci Remote Sens, 2000, 38: 2142–2152CrossRefGoogle Scholar
  2. 2.
    Fornaro G, Serafino F, Lombardini F. Three-dimensional multipass SAR focusing: experiments with long-term spaceborne data. IEEE Trans Geosci Remote Sens, 2005, 43: 702–714CrossRefGoogle Scholar
  3. 3.
    Zhu X, Bamler R. Tomographic SAR inversion by L 1-norm regularization-the compressive sensing approach. IEEE Trans Geosci Remote Sens, 2010, 48: 3839–3846CrossRefGoogle Scholar
  4. 4.
    Bi H, Zhang B C, Hong W. Matrix completion-based distributed compressive sensing for polarimetric SAR tomography. Sci China Inf Sci, 2015, 58: 119301MathSciNetCrossRefGoogle Scholar
  5. 5.
    Nannini M, Scheiber R, Moreira A. Estimation of the minimum number of tracks for SAR tomography. IEEE Trans Geosci Remote Sens, 2009, 47: 531–543CrossRefGoogle Scholar
  6. 6.
    Bi H, Zhang B, Hong W. L q regularization-based unobserved baselines’ data estimation method for tomographic synthetica aperture radar inversion. J Appl Remote Sens, 2016, 10: 035014CrossRefGoogle Scholar
  7. 7.
    Donoho D. Compressed sensing. IEEE Trans Inf Theory, 2006, 52: 1289–1306MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Candès E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math, 2006, 59: 1207–1223MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Nyquist H. Certain topics in telegraph transmission theory. Trans Am Inst Electr Eng, 1928, 47: 617–644CrossRefGoogle Scholar
  10. 10.
    Shannon C. Communication in the presence of noise. Proc Inst Radio Eng, 1949, 37: 10–21MathSciNetGoogle Scholar
  11. 11.
    Zhu X, Bamler R. Very high resolution SAR tomography via compressive sensing. In: Proceedings of Fringe 2009, Frascati, 2009. 1–7Google Scholar
  12. 12.
    Budillon A, Evangelista A, Schirinzi G. SAR tomography from sparse samples. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, 2009. 865–868Google Scholar
  13. 13.
    Aguilera E, Nannini M, Reigber A. Wavelet-based compressed sensing for SAR tomography of forested areas. IEEE Trans Geosci Remote Sens, 2013, 51: 5283–5295CrossRefGoogle Scholar
  14. 14.
    Candès E, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inf Theory, 2006, 52: 5406–5425MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Tropp J. Greed is good: alogrithmic results for sparse approximation. IEEE Trans Inf Theory, 2004, 50: 2231–2242CrossRefMATHGoogle Scholar
  16. 16.
    Bi H, Jiang C, Zhang B, et al. Track distribution optimization for tomographic synthetic aperture radar imaging. J Sys Eng Electron, 2015, 37: 1787–1792Google Scholar
  17. 17.
    Granville V, Krivanek M, Rasson P. Simulated annealing: a proof of convergence. IEEE Trans Pattern Anal Mach Intell, 1994, 16: 652–656CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Hui Bi
    • 1
    • 2
  • Jianguo Liu
    • 3
  • Bingchen Zhang
    • 1
  • Wen Hong
    • 1
  1. 1.Science and Technology on Microwave Imaging Laboratory, Institute of ElectronicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Earth Science and EngineeringImperial College LondonLondonUK

Personalised recommendations