Exploiting group sparsity in SAR tomography

Xiao Xiang Zhu, Nan Ge, Muhammad Shahzad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-20
Number of pages5
ISBN (Electronic)9781479974207
DOIs
StatePublished - 16 Nov 2015
Event3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015 - Pisa, Italy
Duration: 17 Jun 201519 Jun 2015

Publication series

Name2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015

Conference

Conference3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015
Country/TerritoryItaly
CityPisa
Period17/06/1519/06/15

Keywords

  • GIS
  • SAR tomography
  • TanDEM-X
  • compressive sensing
  • group sparsity

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