Joint Sparsity in SAR Tomography for Urban Mapping

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