Volume 4 Issue 2
Jul.  2015
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Liao Ming-sheng, Wei Lian-huan, Wang Zi-yun, Timo Balz, Zhang Lu. Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios[J]. Journal of Radars, 2015, 4(2): 123-129. doi: 10.12000/JR15031
Citation: Liao Ming-sheng, Wei Lian-huan, Wang Zi-yun, Timo Balz, Zhang Lu. Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios[J]. Journal of Radars, 2015, 4(2): 123-129. doi: 10.12000/JR15031

Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios

doi: 10.12000/JR15031
  • Received Date: 2015-03-11
  • Rev Recd Date: 2015-04-09
  • Publish Date: 2015-04-28
  • In modern high resolution SAR data, due to the intrinsic side-looking geometry of SAR sensors, layover and foreshortening issues inevitably arise, especially in dense urban areas. SAR tomography provides a new way of overcoming these problems by exploiting the back-scattering property for each pixel. However, traditional non-parametric spectral estimators, e.g. Truncated Singular Value Decomposition (TSVD), are limited by their poor elevation resolution, which is not comparable to the azimuth and slant-range resolution. In this paper, the Compressive Sensing (CS) approach using Basis Pursuit (BP) and TWo-step Iterative Shrinkage/Thresholding (TWIST) are introduced. Experimental studies with real spotlight-mode TerraSAR-X dataset are carried out using both BP and TWIST, to demonstrate the merits of compressive sensing approaches in terms of robustness, computational efficiency, and super-resolution capability.

     

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