  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, fa\c{c}ades and rooftops.
