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Large-Scale DTM Generation From Satellite Data

Duan, Liuyun and Desbrun, Mathieu and Giraud, Anne and Trastour, Frederic and Laurore, Lionel (2019) Large-Scale DTM Generation From Satellite Data. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE , Piscataway, NJ, pp. 1442-1450. ISBN 9781728125060.

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In remote sensing, Digital Terrain Models (DTM) generation is a long-standing problem involving bare-terrain extraction and surface reconstruction to estimate a DTM from a Digital Surface Model (DSM). Most existing methods (including commercial software packages) have difficulty handling large-scale satellite data of inhomogeneous quality and resolution, and often need an expert-driven manual parameter-tuning process for each geographical type of DSM. In this paper we propose an automated and versatile DTM generation method from satellite data that is perfectly suited to large-scale applications. A novel set of feature descriptors based on multiscale morphological analysis are first computed to extract reliable bare-terrain elevations from DSMs. This terrain extraction algorithm is robust to noise and adapts well to local reliefs in both flat and highly mountainous areas. Then, we reconstruct the final DTM mesh using relative coordinates with respect to the sparse elevations previously detected, and induce preservation of geometric details by adapting these coordinates based on local relief attributes. Experiments on worldwide DSMs show the potential of our approach for large-scale DTM generation without parameter tuning. Our system is flexible as well, as it allows for a straightforward integration of multiple external masks (e.g., forest, road line, buildings, lake, etc) to better handle complex cases, resulting in further improvements of the quality of the output DTM.

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Desbrun, Mathieu0000-0003-3424-6079
Additional Information:© 2019 IEEE. Our most sincere thanks go first to Max Budninskiy (Caltech) for helping us testing SAKE interpolation early on. Jonathan Lambert provided crucial help with manual ground-truth DTM generation. Sébastien Tripodi also helped with a variety of technical issues throughout the project. Justin Hyland, Véronique Poujade and Yuliya Tarabalka were also very supportive along the way. Finally, MD acknowledges ShanghaiTech for hosting him during the final editing of this paper.
Record Number:CaltechAUTHORS:20200417-135143025
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Official Citation:L. Duan, M. Desbrun, A. Giraud, F. Trastour and L. Laurore, "Large-Scale DTM Generation From Satellite Data," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 1442-1450; doi: 10.1109/cvprw.2019.00185
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102611
Deposited By: Tony Diaz
Deposited On:17 Apr 2020 21:05
Last Modified:16 Nov 2021 18:13

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