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

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          Abstract

          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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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2015
          : 4
          : 2
          : 123-129
          Affiliations
          [1 ] State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University
          Article
          1a06b0f195084fdfa65946bf758df13a
          10.12000/JR15031

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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