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　Tian Zhou,Zhenyu Yan,Weidong Du,et al.Filtering Multibeam Bathymetric Point Cloud Based on Smoothed Particle Simulation[J].Journal of Marine Science and Application,2026,(5):1500-1517.[doi:10.1007/s11804-025-00755-9]
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Filtering Multibeam Bathymetric Point Cloud Based on Smoothed Particle Simulation

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Title:: Filtering Multibeam Bathymetric Point Cloud Based on Smoothed Particle Simulation

Author(s):: Tian Zhou¹; 2; 3; Zhenyu Yan¹; 2; 3; Weidong Du¹; 2; 3; Chao Xu¹; 2; 3; Weilu Liu¹; 2; 3; Sen Xu⁴

Affilations:

Keywords:: Multibeam bathymetric point cloud; Underwater digital terrain model; Ground filtering; Smoothed particle simulation

DOI:: 10.1007/s11804-025-00755-9

Abstract:: Recently, the underwater digital terrain model (UDTM) has been extensively used for seafloor terrain measurement. Identifying terrain and nonterrain points from an underwater bathymetric point cloud has been a fundamental problem in UDTM modeling. Hence, we propose a new ground filtering method derived from smoothed particle hydrodynamics to distinguish bathymetric points. The proposed method is called smoothed particle simulation filtering (SPSF), which embodies filtering as a matching of a smoothed particle grid to the point-set surface. First, four seafloor terrain features, namely curvature-based shape factor, slope, roughness, and concave–convex coefficient, are extracted from the moving least-square fitting surface of the original point-set surface, and the terrain complexity of each point is computed. Then, an enhanced particle motion model appropriate for ground filtering is introduced. To improve filtering performance, we divide the point-set surface into several subregions and set adaptive parameters for diverse subregions according to the terrain complexity. Finally, subregional point cloud filtering is performed, and each bathymetric point is marked as "terrain" or "nonterrain". To assess the filtering performance, SPSF is applied to process the multibeam bathymetric point cloud data collected around Chi Island in Qingdao, China. Results indicate that compared with the same type of filtering methods, SPSF improves the filtering accuracy by more than 50% and the filtering efficiency by more than 40%. Furthermore, compared with other types of filtering methods, SPSF displays great potential in alleviating over- and under-filtering in seafloor terrains.

References:

[1] Axelsson P (2000) DEM Generation from Laser Scanner Data Using Adaptive TIN Models. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 33(4): 110-117. https://www.scopus.com/pages/publications/85044575962
[2] Becker M, Teschner M (2007) Weakly compressible SPH for free surface flows. the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA’07). Eurographics Association Goslar 209-217. https://dl.acm.org/doi/pdf/10.5555/1272690.1272719
[3] Calder BR, Mayer LA (2003) Automatic processing of high-rate, high-density multibeam echosounder data. Geochem Geophys Geosyst 4(6): 1-22. https://doi.org/10.1029/2002GC000486
[4] Cai SS, Yu SS (2023) Filtering Airborne LiDAR Data in Forested Environments Based on Multi-Directional Narrow Window and Cloth Simulation. Remote Sens 15(5): 1400. https://doi.org/10.3390/rs15051400
[5] Cai SS, Yu SS, Hui ZY, Tang ZZ (2023) ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations Forests 14(8): 1520. https://doi.org/10.3390/f14081520
[6] Cutter GR (2005) Seafloor habitat characterization, classification, and maps for the lower Piscataqua River estuary. PhD thesis, University of New Hampshire
[7] Desbrun M, Gascuel MP (1996) Smoothed particles: A new paradigm for animating highly deformable bodies. Boulic, R., Hégron, G. (eds) Computer Animation and Simulation’96. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7486-9_5
[8] Dorai C, Jain AK (1997) COSMOS-A representation scheme for 3D free-form objects. IEEE Trans. Pattern Anal. Mach. Intell. 19(10): 1115-1130. https://doi.org/10.1109/34.625113
[9] Gingold RA, Monaghan JJ (1977) Smoothed particle hydrodynamics: theory and application to non-spherical stars. Mon. Not. R. Astron. Soc. 181(3): 375-389. https://doi.org/10.1093/mnras/181.3.375
[10] Goldman R (2005) Curvature formulas for implicit curves and surfaces. Comput. Aided Geom. Des. 22(7): 632-658. https://doi.org/10.1016/j.cagd.2005.06.005
[11] Guan ML, Cheng YX, Li QQ, Wang CS, Fang X, Yu JW (2019) An Effective Method for Submarine Buried Pipeline Detection via Multi-Sensor Data Fusion. IEEE Access 7: 125300-125309. https://doi.org/10.1109/access.2019.2938264
[12] Hollaus M, Höfle B (2010) Terrain roughness parameters from full-waveform airborne LiDAR data. ISPRS TC VII Symposium: 100 Years ISPRS, Vienna, Austria, 38(7B): 287-292
[13] Hu H, Ding YL, Zhu Q, Wu B, Lin H, Du ZQ, Zhang YT, Zhang YS (2014) An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy. ISPRS J. Photogramm. Remote Sens. 92: 98-111. https://doi.org/10.1016/j.isprsjprs.2014.02.014
[14] Le Touzé D, Colagrossi A (2025) Smoothed particle hydrodynamics for free-surface and multiphase flows: a review. Rep Prog Phys. 88(3). https://doi.org/10.1016/10.1088/1361-6633/ada80f
[15] Liang GL, Zhang BY, QI B, Hao Y, Du ZY, LI X (2024) Underwater multitarget fusion tracking method for passive sonar. Acta Acust. 49(3): 501-512. https://doi.org/10.12395/0371-0025.2022188.
[16] Liu X (2008) Airborne LiDAR for DEM generation: Some critical issues. Prog. Phys. Geogr. Earth Environ. 32(1): 31-49. https://doi.org/10.1177/0309133308089496
[17] Lyu XY, Wang L, Huang DX, Wang SL (2023) A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications. Sensors 23(17): 7483. https://doi.org/10.3390/s23177483
[18] Mann M, Agathoklis P, Antoniou A (2001) Automatic outlier detection in multibeam data using median filtering. 2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, BC, Canada, August 2001. https://doi.org/10.1109/PACRIM.2001.953726
[19] Minár J, Evans IS, Jen?o M (2020) A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modeling and prediction. Earth-Sci Rev 211(11). https://doi.org/10.1016/j.earscirev.2020.103414
[20] Mitchell NC (1996) Processing and Analysis of Simrad Multibeam Sonar Data. Mar. Geophys Res 18: 729-739. https://doi.org/10.1007/BF00313883
[21] Monaghan JJ (2005) Smoothed Particle Hydrodynamics. Rep Prog. Phys 68(8): 1703. https://doi.org/10.1088/0034-4885/68/8/R01
[22] Müller M, Charypar D, Gross M (2003) Particle-Based Fluid Simulation for Interactive Applications. the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA’03). Eurographics Association Goslar DEU 154-159. https://dl.acm.org/doi/pdf/10.5555/846276.846298
[23] Palomer A, Rida P, Ribas D, Vallicrosa G (2015) Multi-beam Terrain/Object Classification for Underwater Navigation Correction. OCEANS 2015-Genova Genova Italy 18-21 May. https://doi.org/10.1109/OCEANS-Genova.2015.7271587
[24] Pingel TJ, Clarke KC, McBride WA (2013) "An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS J. Photogramm Remote Sens 77: 21-30. https://doi.org/10.1016/j.isprsjprs.2012.12.002
[25] Pratt JC (2007) Cape Henlopen Spit Complex and Recent Evolution of Breakwater Harbor. University of Delaware. Lewes, Delaware
[26] Rezvani MH, Sabbagh A, Ardalan AA (2015) Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-Estimators. Mar Geod 38: 327-344. https://doi.org/10.1080/01490419.2015. 1053639
[27] Rodríguez-Caballero E, Afana A, Chamizo S, Sol e-Benet A, Canton Y (2016) A new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography. ISPRS J. Photogramm Remote Sens 117: 141-148. https://doi.org/10.1016/j.isprsjprs. 2016.04.004
[28] Schechter H, Bridson R (2012) Ghost SPH for animating water. ACM Trans Graph 31(4): 1-8. https://doi.org/10.1145/2185520.2185557
[29] Shan J, Aparajithan S (2005) Urban DEM generation from raw lidar data. Photogramm. Eng. Remote Sens. 71(2): 217-226. https://doi.org/10.14358/PERS.71.2.217
[30] Susaki J (2012) Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation. Remote Sens 4(6): 1804-1819. https://doi.org/10.3390/rs4061804
[31] Tan YM, Wang S, Xu B, Zhang JB (2018) An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring. ISPRS J. Photogramm Remote Sens 146: 421-429. https://doi.org/10.1016/j.isprsjprs.2018.10.013
[32] Vicki LF (2004) Dynamics of nearshore sedimentary environments revealed through the analysis of multibeam sonar data. State University of New York at Stony Brook
[33] Vosselmann G (2000) Slope based filtering of laser altimetry data. Int. Arch. Photogramm. Remote Sens Spatial Inf Sci 33(Part B3/2): 935-942. https://doi.org/10.1016/S0924-2716(98)00009-4
[34] Wang CK, Tseng YH (2010) Dem generation from airborne LiDAR data by an adaptive dual-directional slope filter. Int. Arch. Photogramm. Remote Sens. Spatial Inf Sci Vienna Austria 38: 628-632. https://api.semanticscholar.org/CorpusID:64620952
[35] Wu Z, Li J, Li S, Shang J, Jin X (2017) A new method to identify the foot of continental slope based on an integrated profile analysis. Mar Geophys Res 38: 199-207. https://doi.org/10.1007/s11001-016-9273-4
[36] Xu C, Wu MX, Zhou T Li JH, Du W.D, Zhang WY, White PR (2020) Optical flow-based detection of gas leaks from pipelines using multibeam water column images. Remote Sens 12(1): 119. https://doi.org/10.3390/rs12010119
[37] Yan ZY, Zhou T, Guo QJ, Xu C, Wang TH, Peng DD, Yu XY (2022) Terrain matching positioning method for underwater vehicles based on curvature discrimination. Ocean Eng 260: 111965. https://doi.org/10.1016/j.oceaneng.2022.111965
[38] Yang AX, Wu ZY, Yang FL, Su DP, Ma Y, Zhao DN, Qi C (2020) Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation. ISPRS J Photogramm Remote Sens 163: 49-61. https://doi.org/10.1016/j.isprsjprs.2020.03.004
[39] Yang P, Qian X (2007) Direct computing of surface curvatures for point-set surfaces. Eurographics Symp. Point-Based Graph 1: 29-36. https://doi.org/10.2312/SPBG/SPBG07/029-036
[40] Zhang K, Chen SC, Whitman D, Shyu ML, Yan J, Zhang C (2003) A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Trans Geosci. Remote Sens 41(4): 872-882. https://doi.org/10.1109/TGRS. 2003.810682
[41] Zhang WM, Qi JB, Wan P, Wang HT, Xie DH, Wang XY, Yan GJ, (2016) An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens 8(6): 501. https://doi.org/10.3390/rs8060501
[42] Zhang WY, Zhou T, Li JH, Xu C (2022) An efficient method for detection and quantitation of underwater gas leakage based on a 300-khz multibeam sonar. Remote Sens 14(17): 4301. https://doi.org/10.3390/rs14174301
[43] Zhou T, Si JK, Wang LY, Xu C, Yu XY (2022) Automatic detection of underwater small targets using forward-looking sonar images. IEEE Trans Geosci Remote Sens 60: 1-12. https://10.1109/TGRS. 2022.3181417

Memo

Memo:: Received date:2025-4-9;Accepted date:2025-8-3。<br>Foundation item:This study is supported by the National Natural Science Foundation of China (Grant number: U2441254, 42176192, 42176188); the Harbin Engineering University Doctoral Research and Innovation Fund (Grant number: XK2050021034); the National Key R&D Program of China (Grant number: 2022YFB3206900); the Stable Supporting Fund of Acoustic Science and Technology (Grant number: JCKYS2022604SSJS002, JCKYS2024604SSJS007); and the Fundamental Research Funds for the Central Universities (Grant number: 3072024CFJ0504).<br>Corresponding author:Weidong Du,E-mail:duweidong@hrbeu.edu.cn

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Last Update: 2026-11-04