Proceedings Article10.1109/IGARSS.2009.5417514
SAR tomography from sparse samples
Alessandra Budillon,Annarita Evangelista,Gilda Schirinzi +2 more
- 12 Jul 2009
- Vol. 4, pp 865-868
TL;DR: This paper proposes a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell.
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Abstract: Three dimensional (3-D) Synthetic Aperture Radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. The practical application of the focusing methods requires that non-uniformly spaced acquisition orbits have to be considered. In this paper we propose a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell. Numerical results on simulated data show the good performance of the method.
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Citations
Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach
Xiao Xiang Zhu,Richard Bamler +1 more
TL;DR: In this article, compressive sensing (CS) methods for tomographic reconstruction of a building complex from the TerraSAR-X spotlight data are presented, and the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods.
Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR
Xiao Xiang Zhu,Richard Bamler +1 more
TL;DR: The SLIMMER algorithm and results are generally applicable to sparse spectral estimation, including SR SAR focus- ing of point-like objects, and are approximately applicable to nonlinear least-squares estimation, and hence they can be considered as a fundamental bound for SR of spectral estimators.
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Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling
TL;DR: A novel 3-D SAR data imaging based on Compressive Sampling theory is presented and allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span.
333
Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment
Xiao Xiang Zhu,Richard Bamler +1 more
TL;DR: The essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis and it is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances.
Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion
Xiao Xiang Zhu,Richard Bamler +1 more
TL;DR: The 4-D point clouds retrieved by VHR TomoSAR has a point density comparable to LiDAR and can be potentially used for dynamic city model reconstruction.
143
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