Exploiting group sparsity in SAR tomography

With meter-resolution images delivered by modern SAR satellites like TerraSAR-X and TanDEM-X, it is now possible to map urban areas from space in very high level of details using advanced interferometric techniques such as persistent scatterer interferometry and tomographic SAR (TomoSAR), whereas these multi-pass interferometric techniques are based on a great number of images. We aim at improving the estimation accuracy of TomoSAR while reducing the required number of images by incorporating prior knowledge of buildings into estimation. In this manuscript, we propose a novel workflow that marries the freely available 2D building footprint GIS data and the group sparsity concept for TomoSAR inversion. Experiments on bistatic SAR data stacks demonstrate great potential of the proposed approach, e.g., highly accurate tomographic reconstruction is achieved using six interferograms only.