Surface-volume-bottom joint-filtering algorithm for Airborne LiDAR bathymetric point cloud

doi:10.11947/j.AGCS.2023.20220248

Abstract

Abstract: The data quality of airborne LiDAR bathymetry (ALB) is affected by many factors (such as sea surface fragmentation waves, floating algae, fish groups, and submarine secondary echoes). To reduce the noise generated by these interferences, a joint-filtering algorithm taking into account the surface, volume, bottom (SVB) is proposed. For water surface noise, the point cloud on the sea surface is separated by building the opposing cloth simulation filter model. Then, the water body outlier is removed by establishing a SOR (statistical outlier removal) filter. Finally, the noise smoothing is performed by building a moving trend surface model for small-scale underwater noise near the terrain body. The ALB data collected in the Jiaozhou Bay area of Qingdao using RIEGL VQ-840-G UAV on-board LiDAR bathymetric system are used to verify the performance of the proposed SVB filtering algorithm. The experimental results show that the overall accuracy and Kappa coefficient of the SVB joint-filtering algorithm can reach 97.45% and 0.947, respectively. It has high efficiency while ensuring the accuracy rate. Compared to the existing algorithms, the proposed filtering algorithm can better solve the problem of ALB point cloud filtering, and can provide an effective solution for ALB bathymetric data point cloud filtering.

Key words: airborne LiDAR bathymetry, SVB joint-filtering, double-layer cloth analog filtering, SOR filtering, mobile trend surface fitting

CLC Number:

P227

SU Dianpeng, YAN Doudou, CHEN Liang, CHEN Yu, DONG Jian, WU Di, YU Xiaolin. Surface-volume-bottom joint-filtering algorithm for Airborne LiDAR bathymetric point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4): 614-623.

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