@inproceedings{147b4445e81e4bc784aaba38aed6d6b8,
title = "Exploiting group sparsity in SAR tomography",
abstract = "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.",
keywords = "GIS, SAR tomography, TanDEM-X, compressive sensing, group sparsity",
author = "Zhu, {Xiao Xiang} and Nan Ge and Muhammad Shahzad",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015 ; Conference date: 17-06-2015 Through 19-06-2015",
year = "2015",
month = nov,
day = "16",
doi = "10.1109/CoSeRa.2015.7330255",
language = "English",
series = "2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "16--20",
booktitle = "2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015",
}