Details
| Original language | English |
|---|---|
| Title of host publication | Image and Signal Processing for Remote Sensing XX |
| Editors | Jon Atli Benediktsson, Francesca Bovolo, Lorenzo Bruzzone |
| Publisher | SPIE |
| ISBN (electronic) | 9781628413076 |
| Publication status | Published - 23 Oct 2014 |
| Event | Image and Signal Processing for Remote Sensing XX - Amsterdam, Netherlands Duration: 22 Sept 2014 → 24 Sept 2014 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 9244 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
Road databases are known to be an important part of any geodata infrastructure, e.g. As the basis for urban planning or emergency services. Updating road databases for crisis events must be performed quickly and with the highest possible degree of automation. We present a semi-automatic algorithm for road verification using textured 3D city models, starting from aerial or even UAV-images. This algorithm contains two processes, which exchange input and output, but basically run independently from each other. These processes are textured urban terrain reconstruction and road verification. The first process contains a dense photogrammetric reconstruction of 3D geometry of the scene using depth maps. The second process is our core procedure, since it contains various methods for road verification. Each method represents a unique road model and a specific strategy, and thus is able to deal with a specific type of roads. Each method is designed to provide two probability distributions, where the first describes the state of a road object (correct, incorrect), and the second describes the state of its underlying road model (applicable, not applicable). Based on the Dempster-Shafer Theory, both distributions are mapped to a single distribution that refers to three states: correct, incorrect, and unknown. With respect to the interaction of both processes, the normalized elevation map and the digital orthophoto generated during 3D reconstruction are the necessary input-together with initial road database entries-for the road verification process. If the entries of the database are too obsolete or not available at all, sensor data evaluation enables classification of the road pixels of the elevation map followed by road map extraction by means of vectorization and filtering of the geometrically and topologically inconsistent objects. Depending on the time issue and availability of a geo-database for buildings, the urban terrain reconstruction procedure has semantic models of buildings, trees, and ground as output.Building s and ground are textured by means of available images. This facilitates the orientation in the model and the interactive verification of the road objects that where initially classified as unknown. The three main modules of the texturing algorithm are: Pose estimation (if the videos are not geo-referenced), occlusion analysis, and texture synthesis.
Keywords
- Building reconstruction, Dempster-shafer theory, Road database, Texturing, Urban terrain, Verification
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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- Harvard
- Apa
- Vancouver
- BibTeX
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Image and Signal Processing for Remote Sensing XX. ed. / Jon Atli Benediktsson; Francesca Bovolo; Lorenzo Bruzzone. SPIE, 2014. 92440T (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9244).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Very fast road database verification using textured 3D city models obtained from airborne imagery
AU - Bulatov, Dimitri
AU - Ziems, Marcel
AU - Rottensteiner, Franz
AU - Pohl, Melanie
PY - 2014/10/23
Y1 - 2014/10/23
N2 - Road databases are known to be an important part of any geodata infrastructure, e.g. As the basis for urban planning or emergency services. Updating road databases for crisis events must be performed quickly and with the highest possible degree of automation. We present a semi-automatic algorithm for road verification using textured 3D city models, starting from aerial or even UAV-images. This algorithm contains two processes, which exchange input and output, but basically run independently from each other. These processes are textured urban terrain reconstruction and road verification. The first process contains a dense photogrammetric reconstruction of 3D geometry of the scene using depth maps. The second process is our core procedure, since it contains various methods for road verification. Each method represents a unique road model and a specific strategy, and thus is able to deal with a specific type of roads. Each method is designed to provide two probability distributions, where the first describes the state of a road object (correct, incorrect), and the second describes the state of its underlying road model (applicable, not applicable). Based on the Dempster-Shafer Theory, both distributions are mapped to a single distribution that refers to three states: correct, incorrect, and unknown. With respect to the interaction of both processes, the normalized elevation map and the digital orthophoto generated during 3D reconstruction are the necessary input-together with initial road database entries-for the road verification process. If the entries of the database are too obsolete or not available at all, sensor data evaluation enables classification of the road pixels of the elevation map followed by road map extraction by means of vectorization and filtering of the geometrically and topologically inconsistent objects. Depending on the time issue and availability of a geo-database for buildings, the urban terrain reconstruction procedure has semantic models of buildings, trees, and ground as output.Building s and ground are textured by means of available images. This facilitates the orientation in the model and the interactive verification of the road objects that where initially classified as unknown. The three main modules of the texturing algorithm are: Pose estimation (if the videos are not geo-referenced), occlusion analysis, and texture synthesis.
AB - Road databases are known to be an important part of any geodata infrastructure, e.g. As the basis for urban planning or emergency services. Updating road databases for crisis events must be performed quickly and with the highest possible degree of automation. We present a semi-automatic algorithm for road verification using textured 3D city models, starting from aerial or even UAV-images. This algorithm contains two processes, which exchange input and output, but basically run independently from each other. These processes are textured urban terrain reconstruction and road verification. The first process contains a dense photogrammetric reconstruction of 3D geometry of the scene using depth maps. The second process is our core procedure, since it contains various methods for road verification. Each method represents a unique road model and a specific strategy, and thus is able to deal with a specific type of roads. Each method is designed to provide two probability distributions, where the first describes the state of a road object (correct, incorrect), and the second describes the state of its underlying road model (applicable, not applicable). Based on the Dempster-Shafer Theory, both distributions are mapped to a single distribution that refers to three states: correct, incorrect, and unknown. With respect to the interaction of both processes, the normalized elevation map and the digital orthophoto generated during 3D reconstruction are the necessary input-together with initial road database entries-for the road verification process. If the entries of the database are too obsolete or not available at all, sensor data evaluation enables classification of the road pixels of the elevation map followed by road map extraction by means of vectorization and filtering of the geometrically and topologically inconsistent objects. Depending on the time issue and availability of a geo-database for buildings, the urban terrain reconstruction procedure has semantic models of buildings, trees, and ground as output.Building s and ground are textured by means of available images. This facilitates the orientation in the model and the interactive verification of the road objects that where initially classified as unknown. The three main modules of the texturing algorithm are: Pose estimation (if the videos are not geo-referenced), occlusion analysis, and texture synthesis.
KW - Building reconstruction
KW - Dempster-shafer theory
KW - Road database
KW - Texturing
KW - Urban terrain
KW - Verification
UR - http://www.scopus.com/inward/record.url?scp=84923064730&partnerID=8YFLogxK
U2 - 10.1117/12.2066823
DO - 10.1117/12.2066823
M3 - Conference contribution
AN - SCOPUS:84923064730
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Image and Signal Processing for Remote Sensing XX
A2 - Benediktsson, Jon Atli
A2 - Bovolo, Francesca
A2 - Bruzzone, Lorenzo
PB - SPIE
T2 - Image and Signal Processing for Remote Sensing XX
Y2 - 22 September 2014 through 24 September 2014
ER -