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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Bibliographic Details
Published inJournal of marine science and engineering Vol. 11; no. 4; p. 770
Main Authors Fan, Jinchang, Pei, Hailong, Lian, Zengjie
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
Subjects
Aerial surveys
Algorithms
background identification
Bathymeters
Bathymetry
Cameras
cBathy
Coastal zone
Coastal zones
Coasts
Correlation analysis
Cross correlation
Drone aircraft
Field of view
Global positioning systems
GPS
Optimization
Propagation
Stabilization
Stitching
Surveying
Surveys
Time series
Topography
UAV
Unmanned aerial vehicles
Video
video stitching
Water depth
Wave velocity
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Summary: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.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse11040770