Registration of Airborne LiDAR Bathymetry and Multibeam Echo Sounder Point Clouds

Airborne light detection and ranging (LiDAR) bathymetry (ALB) and multibeam echo sounder (MBES) are both active remote sensing technologies that are complementary in terms of survey scope. The registration of ALB and MBES data can provide complete overwater and underwater geoinformation on a measure...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Wang, Xiankun, Yang, Fanlin, Zhang, Hande, Su, Dianpeng, Wang, Zhiliang, Xu, Fangzheng
Format: Article
Language:English
Subjects:
Airborne laser scanning (ALS)
Bathymeters
Bathymetry
data gaps
Data mining
Datasets
Echo sounding
Echoes
Feature extraction
Geomagnetism
Laser radar
Lidar
Minimization
multibeam echo sounder (MBES)
Parameter identification
Registration
Remote sensing
Root-mean-square errors
Surveying
Three-dimensional displays
Tin
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Summary:Airborne light detection and ranging (LiDAR) bathymetry (ALB) and multibeam echo sounder (MBES) are both active remote sensing technologies that are complementary in terms of survey scope. The registration of ALB and MBES data can provide complete overwater and underwater geoinformation on a measurement target. However, in the overlapping area of the ALB and MBES data, there are different point densities and few identifiable structure features. Although the existing multiplatform registration strategies can provide good results for overwater datasets, they are difficult to adapt for the registration of ALB and MBES data. Therefore, to address these problems, a new registration method for ALB and MBES datasets is proposed in this letter. First, a triangulated irregular network (TIN) is constructed with control points extracted from the MBES data. Then, the features of the TIN facets are extracted to identify the data gaps. Finally, the transformation parameters are iteratively calculated by minimizing the distances between the ALB points and MBES TIN facets. Five samples with different characteristics captured around Yuanzhi Island in the South China Sea are selected to evaluate the performance of the proposed method. The mean root mean square error (RMSE) of the five samples is approximately 0.2 m. The results indicate that the proposed method performs well for the registration of ALB and MBES datasets, with advantages in accuracy and robustness.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3076462