Abstract
Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.
Highlights
1.1 3D Surface Reconstruction with Satellite ImageryThe creation of large-scale environment reconstructions is relevant for several application areas such as urban planning or environmental monitoring
Since the Rational Polynomial Camera (RPC) models are geo-registered with a set of image specific ground control points, we are able to project the area of interest to each panchromatic and multispectral image pair
This paper presents an approach to reconstruct textured watertight meshes for multi-date satellite imagery including a detailed description of different pipeline steps and their dependencies
Summary
The creation of large-scale environment reconstructions is relevant for several application areas such as urban planning or environmental monitoring. While there is a variety of modalities and sensors to compute such models, the usage of observation satellites has recently gained significant attraction because of the progress made in the field of image-based reconstruction. In contrast to pictures of ground-level and airborne devices, satellite images cover huge areas, which allow for reconstructing large-scale environment models with less effort. In contrast to active sensors like Lidar or Radar, image data inherently represents appearance information, which allows image-based reconstruction pipelines to derive geometry, and the corresponding texture information. In comparison to point clouds, textured meshes oftentimes create a similar appearance with a lower information redundancy and inherently contain specific properties that simplify the computation of consistent visualizations or intersection tests at different scales
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
