Tomographic analysis of high-rise buildings using TerraSAR-X spotlight data with compressive sensing approach

Modern spaceborne SAR sensors provide geometric resolutions well below one meter. In data of this kind, many features of urban objects become visible. However, because of 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 are limited by their poor elevation resolution, which is not comparable to the azimuth and slant-range resolution. In order to improve the estimated elevation resolution, super-resolution techniques, like compressive sensing, are introduced to SAR tomographic processing. In this paper, we analyze the performance of the compressive sensing approach in SAR tomographic analysis. Numerical experiments on simulated signals and real TerraSAR-X spotlight data are given, which demonstrate the robustness and super-resolution power of compressive sensing.