Polarization differential SAR tomography imaging method based on group sparse

被引：0
|
作者：
Yang M. [1 ,2 ,3 ]
Zhang B. [1 ,2 ]
Wei Z. [1 ,2 ,3 ]
Xu Z. [1 ,2 ,3 ]
Hong W. [1 ,2 ]
机构：
[1] Key Laboratory of Technology in Geospatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing
[2] Institute of Electronics, Chinese Academy of Sciences, Beijing
[3] University of Chinese Academy of Sciences, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 05期
关键词：
Differential synthetic aperture radar (SAR) tomography; Full polarization; Group sparse; Threshold iteration;
D O I：
10.3969/j.issn.1001-506X.2019.05.11
中图分类号：
学科分类号：
摘要：
Differential synthetic aperture radar (SAR) tomography reconstructs the backscattering coefficient and line-of-sight deformation rate of the target by multi-pass data. We combine the full-polarization and differential SAR tomography. Aiming at urban building imaging elevation sparseness and the same signal sparse support set in full-polarization data inversion, a solution model combined with sparse constraints and group sparse constraints is solved by threshold iterative method based on hierarchical sparseness. The simulation and semi-simulation experiment based on BioSAR data are done to verify the results. The experiment shows compared with the monopole the reconstruction results the full-polarization differential SAR tomography method improve the accuracy of elevation and has better robustness. When the signal-to-noise ratio is 10 dB, it can recover the elevation information and deformation rate better than the single-polarization differential SAR tomography method. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：1007 / 1012
页数：5
相关论文
共 15 条
  • [1] Knaell K.K., Cardillo G.P., Radar tomography for the generation of three-dimensional images, IEE Proceedings Radar, Sonar and Navigation, 142, 2, pp. 54-60, (1995)
  • [2] Lombardini F., Differential tomography: a new framework for SAR interferometry, IEEE Trans. on Geoscience and Remote Sensing, 43, 1, pp. 37-44, (2005)
  • [3] Serafino F., Soldovieri F., Lombardini F., Et al., Singular value decomposition applied to 4D SAR imaging, Proc. of the IEEE International Geoscience and Remote Sensing Symposium, pp. 2701-2704, (2005)
  • [4] Ren X.Z., Yang R.L., An inverse problem based approach for differential SAR tomography imaging, Journal of Electronics & Information Technology, 32, 3, pp. 582-586, (2010)
  • [5] Zhao K.X., Bi H., Zhang B.C., SAR tomography processing method based on fast iterative shrinkage-thrsholding, Systems Engineering and Electronics, 39, 5, pp. 1019-1023, (2017)
  • [6] Pang B., Dai D.H., Liu J., Et al., A super-resolving imaging algorithm of forward-looking SAR based on compressive sensing, Proc. of the Geoscience and Remote Sensing Symposium, pp. 572-575, (2014)
  • [7] Zhu X., Bamler R., Super-resolution for 4-D SAR tomography via compressive sensing, Proc. of the European Conference on Synthetic Aperture Radar, pp. 1-4, (2011)
  • [8] Budillon A., Evangelista A., Schirinzi G., Three-dimensional SAR focusing from multipass signals using compressive sampling, IEEE Trans. on Geoscience & Remote Sensing, 49, 1, pp. 488-499, (2011)
  • [9] Donoho D.L., Compressed sensing, IEEE Trans. on Information Theory, 52, 4, pp. 1289-1306, (2006)
  • [10] Cloude S., Polarisation: Applications in Remote Sensing, (2010)
12