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

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.

Journal

Title: Journal of Radars

Publisher: Chinese Academy of Sciences

Publication date (Electronic): 01 April 2015

Volume: 4

Issue: 2

Pages: 123-129

Affiliations

[1 ] State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University

Article

Doaj ID: 1a06b0f195084fdfa65946bf758df13a

DOI: 10.12000/JR15031

License:

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/

Product

Self URI: http://radars.ie.ac.cn/CN/html/R15031.htm

Self URI (journal page): http://radars.ie.ac.cn/EN/volumn/home.shtml

Categories

Subject: Technology (General)

Subject: T1-995