Surveying of Nearshore Bathymetry Using UAVs Video Stitching
In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/4/770 |
| _version_ | 1797604796207726592 |
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| author | Jinchang Fan Hailong Pei Zengjie Lian |
| author_facet | Jinchang Fan Hailong Pei Zengjie Lian |
| author_sort | Jinchang Fan |
| collection | DOAJ |
| description | In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms. |
| first_indexed | 2024-03-11T04:51:50Z |
| format | Article |
| id | doaj.art-3c9078864b9142c0be2547e04eda03e8 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T04:51:50Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-3c9078864b9142c0be2547e04eda03e82023-11-17T19:55:47ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-03-0111477010.3390/jmse11040770Surveying of Nearshore Bathymetry Using UAVs Video StitchingJinchang Fan0Hailong Pei1Zengjie Lian2Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, ChinaKey Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, ChinaKey Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, ChinaIn this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms.https://www.mdpi.com/2077-1312/11/4/770bathymetryvideo stitchingUAVbackground identificationcBathy |
| spellingShingle | Jinchang Fan Hailong Pei Zengjie Lian Surveying of Nearshore Bathymetry Using UAVs Video Stitching Journal of Marine Science and Engineering bathymetry video stitching UAV background identification cBathy |
| title | Surveying of Nearshore Bathymetry Using UAVs Video Stitching |
| title_full | Surveying of Nearshore Bathymetry Using UAVs Video Stitching |
| title_fullStr | Surveying of Nearshore Bathymetry Using UAVs Video Stitching |
| title_full_unstemmed | Surveying of Nearshore Bathymetry Using UAVs Video Stitching |
| title_short | Surveying of Nearshore Bathymetry Using UAVs Video Stitching |
| title_sort | surveying of nearshore bathymetry using uavs video stitching |
| topic | bathymetry video stitching UAV background identification cBathy |
| url | https://www.mdpi.com/2077-1312/11/4/770 |
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