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Tomographic synthetic aperture radar (SAR) inversion, including SAR tomography and differential SAR tomography, is essentially a spectral analysis problem. The resolution in the elevation direction depends on the elevation aperture size, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, such as 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 (SR) reconstruction algorithms are desired. Considering the sparsity of the signal in elevation, a compressive sensing based super-resolving algorithm, named “Scale-down by L1 norm Minimization, Model selection, and Estimation Reconstruction” (SL1MMER, pronounced “slimmer”), was proposed by the authors in a previous paper. The ultimate bounds of the technique on localization accuracy and SR power were investigated. In this paper, the essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis. It is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances. Furthermore, the SR capability of SL1MMER is demonstrated using TerraSAR-X real data examples. For a high rise building complex, the percentage of detected double scatterers is almost doubled compared to classical linear estimators. Among them, half of the detected double scatterer pairs have elevation distances smaller than the Rayleigh elevation resolution. This confirms the importance of SR for this type of applications.