Contributors: Laboratoire Traitement et Communication de l'Information (LTCI); Institut Mines-Télécom Paris (IMT)-Télécom Paris; Laboratoire Hubert Curien (LabHC); Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS); Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES); Institut Mines-Télécom Paris (IMT)-Télécom Paris-Institut Mines-Télécom Paris (IMT)-Télécom Paris; Département Images, Données, Signal (IDS); Télécom ParisTech; DEMR, ONERA, Université Paris Saclay (COmUE) Palaiseau; ONERA-Université Paris Saclay (COmUE); Institut d'Électronique et des Technologies du numéRique (IETR); Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS); Agence Nationale de la Recherche, Not AvailableDirection Générale de l’Armement, DGADélégation Générale pour l'Armement, DGAANR-15-ASTR-0002; ANR-15-ASTR-0002,ALYS,Analyse de surfaces urbaines par tomographie SAR(2015)
نبذة مختصرة : International audience ; 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.
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