Urban surface reconstruction in SAR tomography by graph-cuts

Abstract : SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from stacks of SAR images. High resolution satellites such as TerraSAR-X provide images that can be combined to produce 3-D models. In urban areas, sparsity priors are generally enforced during the tomographic inversion process in order to retrieve the location of scatterers seen within a given radar resolution cell. However, such priors often miss parts of the urban surfaces. Those missing parts are typically regions of flat areas such as ground or rooftops. This paper introduces a surface segmentation algorithm based on the computation of the optimal cut in a flow network. This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces. Illustrations on a TerraSAR-X tomographic dataset demonstrate the potential of the approach to produce a 3-D model of urban surfaces such as ground, façades and rooftops. © 2019 Elsevier Inc.
Document type :
Journal articles
Complete list of metadatas

https://hal-univ-rennes1.archives-ouvertes.fr/hal-02309547
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, October 9, 2019 - 12:59:28 PM
Last modification on : Thursday, November 21, 2019 - 5:56:16 PM

Links full text

Identifiers

Citation

C. Rambour, L. Denis, Florence Tupin, H. Oriot, Y. Huang, et al.. Urban surface reconstruction in SAR tomography by graph-cuts. Computer Vision and Image Understanding, Elsevier, 2019, 188, pp.102791. ⟨10.1016/j.cviu.2019.07.011⟩. ⟨hal-02309547⟩

Share

Metrics

Record views

86