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

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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 N − M 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.

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References

  1. Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans Geosci Remote Sens, 2000, 38: 2142–2152

    Article  Google Scholar 

  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: 702–714

    Article  Google Scholar 

  3. Zhu X, Bamler R. Tomographic SAR inversion by L 1-norm regularization-the compressive sensing approach. IEEE Trans Geosci Remote Sens, 2010, 48: 3839–3846

    Article  Google Scholar 

  4. Bi H, Zhang B C, Hong W. Matrix completion-based distributed compressive sensing for polarimetric SAR tomography. Sci China Inf Sci, 2015, 58: 119301

    Article  MathSciNet  Google Scholar 

  5. Nannini M, Scheiber R, Moreira A. Estimation of the minimum number of tracks for SAR tomography. IEEE Trans Geosci Remote Sens, 2009, 47: 531–543

    Article  Google Scholar 

  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: 035014

    Article  Google Scholar 

  7. Donoho D. Compressed sensing. IEEE Trans Inf Theory, 2006, 52: 1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  8. Candès E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math, 2006, 59: 1207–1223

    Article  MathSciNet  MATH  Google Scholar 

  9. Nyquist H. Certain topics in telegraph transmission theory. Trans Am Inst Electr Eng, 1928, 47: 617–644

    Article  Google Scholar 

  10. Shannon C. Communication in the presence of noise. Proc Inst Radio Eng, 1949, 37: 10–21

    MathSciNet  Google Scholar 

  11. Zhu X, Bamler R. Very high resolution SAR tomography via compressive sensing. In: Proceedings of Fringe 2009, Frascati, 2009. 1–7

    Google Scholar 

  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. 865–868

    Google Scholar 

  13. Aguilera E, Nannini M, Reigber A. Wavelet-based compressed sensing for SAR tomography of forested areas. IEEE Trans Geosci Remote Sens, 2013, 51: 5283–5295

    Article  Google Scholar 

  14. Candès E, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inf Theory, 2006, 52: 5406–5425

    Article  MathSciNet  MATH  Google Scholar 

  15. Tropp J. Greed is good: alogrithmic results for sparse approximation. IEEE Trans Inf Theory, 2004, 50: 2231–2242

    Article  MATH  Google Scholar 

  16. Bi H, Jiang C, Zhang B, et al. Track distribution optimization for tomographic synthetic aperture radar imaging. J Sys Eng Electron, 2015, 37: 1787–1792

    Google Scholar 

  17. Granville V, Krivanek M, Rasson P. Simulated annealing: a proof of convergence. IEEE Trans Pattern Anal Mach Intell, 1994, 16: 652–656

    Article  Google Scholar 

Download references

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.

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Authors and Affiliations

  1. Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China

    Hui Bi, Bingchen Zhang & Wen Hong

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hui Bi

  3. Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK

    Jianguo Liu

Authors
  1. Hui Bi
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  2. Jianguo Liu
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  3. Bingchen Zhang
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  4. Wen Hong
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Correspondence to Hui Bi.

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Conflict of interest The authors declare that they have no conflict of interest.

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Bi, H., Liu, J., Zhang, B. et al. Baseline distribution optimization and missing data completion in wavelet-based CS-TomoSAR. Sci. China Inf. Sci. 61, 042302 (2018). https://doi.org/10.1007/s11432-016-9068-y

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  • DOI: https://doi.org/10.1007/s11432-016-9068-y

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