Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

作者：Xiao Xiang Zhu, Richard Bamler · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2010 · DOI：10.1109/tgrs.2010.2048117 · 被引用次数：537 · 研究领域：Sparse and Compressive Sensing Techniques、Synthetic Aperture Radar (SAR) Applications and Techniques、Microwave Imaging and Scattering Analysis

Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth-range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents 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 properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.