Urban Surface Reconstruction in SAR Tomography by Graph-Cuts - Archive ouverte HAL Access content directly
Journal Articles Computer Vision and Image Understanding Year : 2019

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 seg-mentation 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.
Fichier principal
Vignette du fichier
cviu_2019 (1).pdf (13.12 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01873673 , version 1 (22-11-2019)

Identifiers

Cite

Clément Rambour, Loïc Denis, Florence Tupin, Hélène Oriot, Yue Huang, et al.. Urban Surface Reconstruction in SAR Tomography by Graph-Cuts. Computer Vision and Image Understanding, 2019, 188, pp.102791. ⟨10.1016/j.cviu.2019.07.011⟩. ⟨hal-01873673⟩
240 View
57 Download

Altmetric

Share

Gmail Facebook X LinkedIn More