Urban Surface Reconstruction in SAR Tomography by Graph-Cuts

TL;DR

This paper presents a novel graph-cut based surface segmentation method integrated into SAR tomography to enhance urban surface reconstruction, effectively recovering flat surfaces like ground and rooftops from high-resolution SAR data.

Contribution

It introduces a surface segmentation algorithm using optimal graph cuts within SAR tomography, improving urban surface recovery in 3-D models.

Findings

Effective segmentation of urban surfaces including ground and rooftops.

Improved 3-D urban models from TerraSAR-X data.

Potential to enhance SAR-based urban mapping applications.

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, fa\c{c}ades and rooftops.