Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
This work addresses incomplete surface reconstruction in urban SAR tomography for remote sensing applications, representing an incremental improvement.
The paper tackles the problem of missing flat surfaces like ground and rooftops in urban 3-D reconstruction from SAR tomography by introducing a surface segmentation algorithm based on graph-cuts, demonstrating its potential to improve recovery of urban surfaces such as ground, façades, and rooftops on a TerraSAR-X dataset.
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.