Registration of Urban Aerial Image and LiDAR Based on Line Vectors
In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/7/10/965 |
| _version_ | 1818544348955934720 |
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| author | Qinghong Sheng Qi Wang Xinyue Zhang Bo Wang Bin Zhang Zhengning Zhang |
| author_facet | Qinghong Sheng Qi Wang Xinyue Zhang Bo Wang Bin Zhang Zhengning Zhang |
| author_sort | Qinghong Sheng |
| collection | DOAJ |
| description | In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features. |
| first_indexed | 2024-12-11T22:47:20Z |
| format | Article |
| id | doaj.art-5b2f1844d9a449d5a1ed4292e396fff1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T22:47:20Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5b2f1844d9a449d5a1ed4292e396fff12022-12-22T00:47:34ZengMDPI AGApplied Sciences2076-34172017-09-0171096510.3390/app7100965app7100965Registration of Urban Aerial Image and LiDAR Based on Line VectorsQinghong Sheng0Qi Wang1Xinyue Zhang2Bo Wang3Bin Zhang4Zhengning Zhang5College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaTianjin Key Laboratory of Intelligent Information Processing in Remote Sensing, Tianjin 300301, ChinaIn a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.https://www.mdpi.com/2076-3417/7/10/965image registrationlaser radarmultisensor dataurban areasaerial imagelinear feature |
| spellingShingle | Qinghong Sheng Qi Wang Xinyue Zhang Bo Wang Bin Zhang Zhengning Zhang Registration of Urban Aerial Image and LiDAR Based on Line Vectors Applied Sciences image registration laser radar multisensor data urban areas aerial image linear feature |
| title | Registration of Urban Aerial Image and LiDAR Based on Line Vectors |
| title_full | Registration of Urban Aerial Image and LiDAR Based on Line Vectors |
| title_fullStr | Registration of Urban Aerial Image and LiDAR Based on Line Vectors |
| title_full_unstemmed | Registration of Urban Aerial Image and LiDAR Based on Line Vectors |
| title_short | Registration of Urban Aerial Image and LiDAR Based on Line Vectors |
| title_sort | registration of urban aerial image and lidar based on line vectors |
| topic | image registration laser radar multisensor data urban areas aerial image linear feature |
| url | https://www.mdpi.com/2076-3417/7/10/965 |
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