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 2 m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.
Highlights
Three-dimensional reconstruction from remote sensing data has a range of applications across different fields, such as urban 3D modeling and management, environmental studies, and geographic information systems
We investigated the possibility of stereogrammetric 3D reconstruction from VHR synthetic aperture radar (SAR)-optical image pairs by developing a full 3D reconstruction framework based on the classic photogrammetric workflow
We mathematically proved that the epipolarity constraint can be established for SAR-optical image pairs
Summary
Three-dimensional reconstruction from remote sensing data has a range of applications across different fields, such as urban 3D modeling and management, environmental studies, and geographic information systems. 3D reconstruction in remote sensing is either based on exploiting phase information provided by interferometric SAR, or on space intersection in the frame of photogrammetry with optical images or radargrammetry with SAR image pairs. In all these stereogrammetric approaches, at least two overlapping images are required to extract 3D spatial information. Both photogrammetry and radargrammetry, suffer from several drawbacks. Photogrammetry using high-resolution optical imagery is limited by relatively poor absolute localization accuracy and cloud effects, whereas radargrammetry suffers from the difficulty of image matching for severely different oblique viewing angles
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
