Registration of Airborne LiDAR Bathymetry and Multibeam Echo Sounder Point Clouds

被引:20
作者
Wang, Xiankun [1 ,2 ]
Yang, Fanlin [1 ,3 ]
Zhang, Hande [4 ]
Su, Dianpeng [1 ,3 ,5 ]
Wang, Zhiliang [2 ]
Xu, Fangzheng [1 ]
机构
[1] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
[2] Minist Nat Resource, Key Lab Marine Environm Survey Technol & Applicat, Guangzhou 510300, Peoples R China
[3] Minist Nat Resources China, Key Lab Ocean Geomat, Qingdao 266590, Peoples R China
[4] Air Borne Detachment China Marine Surveillance, Qingdao 266061, Peoples R China
[5] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China
基金
中国国家自然科学基金;
关键词
Tin; Three-dimensional displays; Feature extraction; Geomagnetism; Laser radar; Data mining; Minimization; Airborne laser scanning (ALS); data gaps; multibeam echo sounder (MBES); registration;
D O I
10.1109/LGRS.2021.3076462
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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.
引用
收藏
页数:5
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