A Framework for SAR-Optical Stereogrammetry over Urban Areas

TL;DR

This paper explores a stereogrammetry framework for combining very-high-resolution SAR and optical images to generate accurate 3D urban models, introducing methods to improve matching and geolocation accuracy.

Contribution

It presents a novel pipeline applying semi-global matching and epipolar constraints to SAR-optical pairs, enhancing 3D reconstruction over urban areas.

Findings

Median point cloud accuracy of about 2 meters achieved

Epipolarity constraints improve matching reliability

Multi-sensor block adjustment enhances geolocation accuracy

Abstract

Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.