Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Matrix completion-based distributed compressive sensing for polarimetric SAR tomography

基于矩阵补偿的分布式压缩感知层析合成孔径雷达成像方法

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

摘要

创新点

本文提出了一种基于矩阵补偿的分布式压缩感知层析合成孔径雷达成像方法。该方法首先利用矩阵补偿对不同极化通道的未知基线数据进行估计, 然后基于补偿后的数据, 利用分布式压缩感知对高程向进行重建。相比于传统的压缩感知和分布式压缩感知技术, 在不改变高程向孔径大小的前提下, 本文方法可以提升高程向重建质量,这意味着减少层析合成孔径雷达成像中获取基线的数目, 从而降低飞行成本和时间消耗成为可能。本文的主要创新点是:

  1. (a)

    一种全新的利用矩阵补偿对未知极化层析合成孔径雷达观测数据进行恢复的方法;

  2. (b)

    基于补偿数据的高程向分布式压缩感知重建的实现。

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

References

  1. 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 

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

    Article  MathSciNet  MATH  Google Scholar 

  3. Zhang B C, Hong W, Wu Y R. Sparse microwave imaging: principles and applications. Sci China Inf Sci, 2012, 55: 1722–1754

    Article  MathSciNet  MATH  Google Scholar 

  4. Duarte F, Sarvotham S, Baron D, et al. Distributed compressed sensing of jointly sparse signals. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2005. 1537–1541

    Google Scholar 

  5. Aguilera E, Nannini M, Reigber A. Multisignal compressed sensing for polarimetric SAR tomography. IEEE Geosci Remote Sens Lett, 2012, 9: 871–875

    Article  Google Scholar 

  6. Candès E, Recht B. Exact matrix completion via convex optimization. Found Comput Math, 2009, 9: 717–772

    Article  MathSciNet  MATH  Google Scholar 

  7. Bi H, Jiang C, Zhang B, et al. Radar change imaging with undersampled data based on matrix completion and bayesian compressive sensing. IEEE Geosci Remote Sens Lett, 2015, 12: 1546–1550

    Article  Google Scholar 

  8. Hajnsek I, Scheiber R, Ulander L, et al. BioSAR 2007 Technical Assistance for the Development of Airborne SAR and Geophysical Measurements During the BioSAR 2007 Experiment: Final Report without Synthesis. European Space Agency Technical Report 20755/07/nl/cb, 2008

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Bi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bi, H., Zhang, B. & Hong, W. Matrix completion-based distributed compressive sensing for polarimetric SAR tomography. Sci. China Inf. Sci. 58, 1–3 (2015). https://doi.org/10.1007/s11432-015-5395-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-015-5395-6

关键词