Skip to main content
SpringerLink
Log in

Off-grid correction for improving scatterer localization performance in compressive sampling SAR tomography

  • Letter
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Fornaro G, Serafino F. Imaging of single and double scatterers in urban areas via SAR tomography. IEEE Trans Geosci Remote Sens, 2006, 44: 3497–3505

    Article  Google Scholar 

  2. Zhu X X, Bamler R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR. IEEE Trans Geosci Remote Sens, 2012, 50: 247–258

    Article  Google Scholar 

  3. Zhu X X, Dong Z, Yu A X, et al. New approaches for robust and efficient detection of persistent scatterers in SAR tomography. Remote Sens, 2019, 11: 356

    Article  Google Scholar 

  4. Luo H, Li Z H, Dong Z, et al. Super-resolved multiple scatterers detection in SAR tomography based on compressive sensing generalized likelihood ratio test (CS-GLRT). Remote Sens, 2019, 11: 1930

    Article  Google Scholar 

  5. Luo H, Dong Z, Li Z H, et al. Iterative-SGLRT for multiple-scatterer detection in SAR tomography. IEEE Geosci Remote Sens Lett, 2020. doi: https://doi.org/10.1109/LGRS.2020.2964645

  6. Chi Y J, Scharf L L, Pezeshki A, et al. Sensitivity to basis mismatch in compressed sensing. IEEE Trans Signal Process, 2011, 59: 2182–2195

    Article  MathSciNet  Google Scholar 

  7. Budillon A, Ferraioli G, Schirinzi G. Localization performance of multiple scatterers in compressive sampling SAR tomography: results on COSMO-SkyMed data. IEEE J Sel Top Appl Earth Obs Remote Sens, 2014, 7: 2902–2910

    Article  Google Scholar 

  8. Zhu X X. Very high resolution tomographic SAR inversion for urban infrastructure monitoring-a sparse and nonlinear tour. Dissertation for Ph.D. Degree. Mnchen: Technische Universitt Mnchen, 2011

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61771478). The authors would like to thank the German Aerospace Center (DLR) for providing high-resolution terraSAR-X dataset. We are also grateful for the anonymous reviewers for their valuable suggestions.

Author information

Authors and Affiliations

  1. College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China

    Xiaoxiang Zhu, Anxi Yu & Zhen Dong

  2. China Electronic Technology Group Corporation (CETC), Beijing, 100846, China

    Manqing Wu

  3. China Xi’an Satellite Control Center (XSCC), Xi’an, 710000, China

    Qi Liu

Authors
  1. Xiaoxiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anxi Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manqing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen Dong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, X., Yu, A., Dong, Z. et al. Off-grid correction for improving scatterer localization performance in compressive sampling SAR tomography. Sci. China Inf. Sci. 64, 129302 (2021). https://doi.org/10.1007/s11432-020-2926-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2926-7

Search

Navigation