YANG Anxiu, WU Ziyin, YANG Fanlin, SU Dianpeng, FENG Chengkai, XU Fangzheng. An Automatic Filtering Algorithm of Multi-beam Bathymetry Based on Bidirectional Cloth Simulation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(4): 517-525. DOI: 10.13203/j.whugis20190419
Citation: YANG Anxiu, WU Ziyin, YANG Fanlin, SU Dianpeng, FENG Chengkai, XU Fangzheng. An Automatic Filtering Algorithm of Multi-beam Bathymetry Based on Bidirectional Cloth Simulation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(4): 517-525. DOI: 10.13203/j.whugis20190419

An Automatic Filtering Algorithm of Multi-beam Bathymetry Based on Bidirectional Cloth Simulation

Funds: 

The National Natural Science Foundation of China 52001189

The National Natural Science Foundation of China 41830540

The National Natural Science Foundation of China 41930535

Open Foundation of Key Laboratory of Submarine Geosciences, MNR KLSG2106

SDUST Research Fund 2019TDJH103

Scientific Research Fund of the Second Institute of Oceanography, MNR JZ1902

National Program on Global Change and Air-Sea Interaction Special Project GASI-EOGE-01

More Information
  • Author Bio:

    YANG Anxiu, PhD, majors in seabed topography survey technology and scientific research.E-mail: skyanganxiu@163.com

  • Corresponding author:

    WU Ziyin, PhD, professor.E-mail: zywu@vip.163.com

  • Received Date: July 11, 2020
  • Published Date: April 04, 2022
  •   Objectives  To overcome the problem that the current bathymetric filtering methods require manual intervention and are difficult to implement technically, a bidirectional cloth simulation filtering (BCSF) algorithm is proposed and implemented in this paper.
      Methods  Firstly, the transfer iterative trend surface is established to eliminate the negative anomalies and guarantee the continuous expression of the seafloor topography. Then, the filtering surface is established to solve the over-filtering problem of convex and concave seafloor topographies based on the proposed BCSF correction model. Finally, to further improve the effectiveness of the filtering, adaptive distance threshold is optimized and estimated. To evaluate the performance of the proposed algorithm, the BCSF algorithm is applied to shallow water multibeam bathymetry data.
      Results  The experimental results show that the BCSF algorithm can avoid the over-filtering. The elimination rate of the proposed BCSF algorithm is better than that of the CSF (cloth simulation filtering) algorithm, which decreases from 12.87% to 0.76% for the whole study area and from 15.29% to 1.09% for local study area, respectively.
      Conclusions  Compared with the CUBE (combined uncertainty bathymetry estimation) algorithm, the BCSF algorithm is more easily to implement and can retain more terrain details. Consequently, the BCSF algorithm has strong robustness and application prospects for multibeam bathymetry data.
  • [1]
    李家彪. 多波束勘测原理、技术和方法[M]. 北京: 海洋出版社, 1999

    Li Jiabiao. Multibeam Sounding Principles, Survey Technologies and Data Processing Methods[M]. Beijing: Ocean Press, 1999
    [2]
    赵建虎. 现代海洋测绘[M]. 武汉: 武汉大学出版社, 2008

    Zhao Jianhu. Modern Marine Surveying and Charting[M]. Wuhan: Wuhan University Press, 2008
    [3]
    Liu Y, Wu Z Y, Zhao D N, et al. Construction of High-Resolution Bathymetric Dataset for the Mariana Trench[J]. IEEE Access, 2019, 7: 14241-14250
    [4]
    Yin S R, Lin L, Pope E L, et al. Continental Slope-Confined Canyons in the Pearl River Mouth Basin in the South China Sea Dominated by Erosion, 2004-2018[J]. Geomorphology, 2019, 344: 60-74 doi: 10.1016/j.geomorph.2019.07.016
    [5]
    Zhou J Q, Wu Z Y, Jin X L, et al. Observations and Analysis of Giant Sand Wave Fields on the Taiwan Banks, Northern South China Sea[J]. Marine Geology, 2018, 406: 132-141 doi: 10.1016/j.margeo.2018.09.015
    [6]
    Wu Z Y, Jin X L, Cao Z Y, et al. Distribution, Formation and Evolution of Sand Ridges on the East China Sea Shelf[J]. Science in China Series D: Earth Sciences, 2010, 53(1): 101-112
    [7]
    吴自银, 李家彪, 阳凡林, 等. 一种大陆坡脚点自动识别与综合判断方法[J]. 测绘学报, 2014, 43(2): 170-177 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201402012.htm

    Wu Ziyin, Li Jiabiao, Yang Fanlin, et al. An Intergrated Method for Automatic Identification of the Foot Point of Slope[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(2): 170-177 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201402012.htm
    [8]
    Wang M W, Wu Z Y, Yang F L, et al. Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data Around Yuanzhi Island in the South China Sea[J]. Sensors, 2018, 18(11): 3828 doi: 10.3390/s18113828
    [9]
    Wu Z Y, Li J B, Jin X L, et al. Distribution, Features, and Influence Factors of the Submarine Topographic Boundaries of the Okinawa Trough[J]. Science China Earth Sciences, 2014, 57(8): 1885-1896 doi: 10.1007/s11430-013-4810-3
    [10]
    Wu Z Y, Jin X L, Zhou J Q, et al. Comparison of Buried Sand Ridges and Regressive Sand Ridges on the Outer Shelf of the East China Sea[J]. Marine Geophysical Research, 2017, 38(1): 187-198
    [11]
    阳凡林, 李家彪, 吴自银, 等. 多波束测深瞬时姿态误差的改正方法[J]. 测绘学报, 2009, 38(5): 450-456 doi: 10.3321/j.issn:1001-1595.2009.05.012

    Yang Fanlin, Li Jiabiao, Wu Ziyin, et al. The Methods of Removing Instantaneous Attitude Errors for Multibeam Bathymetry Data[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(5): 450-456 doi: 10.3321/j.issn:1001-1595.2009.05.012
    [12]
    吴自银, 阳凡林, 罗孝文, 等. 高分辨率海底地形地貌——探测处理理论与技术[M]. 北京: 科学出版社, 2018

    Wu Ziyin, Yang Fanlin, Luo Xiaowen, et al. High-Resolution Submarine Topography—Theory and Technology for Surveying and Post-Processing[M]. Beijing: China Science Press, 2018
    [13]
    吴自银, 阳凡林, 李守军, 等. 高分辨率海底地形地貌——可视计算与科学应用[M]. 北京: 科学出版社, 2018

    Wu Ziyin, Yang Fanlin, Li Shoujun, et al. High-Resolution Submarine Topography——Visual Computation and Scientific Applications[M]. Beijing: China Science Press, 2018
    [14]
    赵祥鸿, 暴景阳, 欧阳永忠, 等. 利用BP神经网络剔除多波束测深数据粗差[J]. 武汉大学学报·信息科学版, 2019, 44(4): 518-524 doi: 10.13203/j.whugis20160336

    Zhao Xianghong, Bao Jingyang, Ouyang Yongzhong, et al. Detecting Outlier of Multibeam Sounding with BP Neural Network[J]. Geomatics and Information Science of Wuhan University, 2019, 44(4): 518-524 doi: 10.13203/j.whugis20160336
    [15]
    Guenther G C, Green J E. Improved Depth Selection in the Bathymetric Swath Survey System (BS3) Combined Offline Processing (COP) Algorithm[R]. National Oceanic and Atmospheric Administration, Technical Report OTES-10, Department of Commerce, Rockvill, MD, 1982
    [16]
    Ware C, Slipp L, Wong K W, et al. A System for Cleaning High Volume Bathymetry[J]. International Hydrographic Review, 1992, 69: 77-94
    [17]
    Du Z, Wells D E, Mayer L A. An Approach to Automatic Detection of Outliers in Multibeam Echo Sounding Data[J]. Hydrographic Journal, 1996, 79: 19-25
    [18]
    Eeg J. On the Identification of Spikes in Soundings[J]. International Hydrographic Review, 2015, 72(1): 33-41
    [19]
    Ladner R W, Elmore P, Perkins A L, et al. Automated Cleaning and Uncertainty Attribution of Archival Bathymetry Based on a Priori Knowledge[J]. Marine Geophysical Research, 2017, 38(3): 291-301 doi: 10.1007/s11001-017-9304-9
    [20]
    Mann M, Agathoklis P, Antoniou A. Automatic Outlier Detection in Multibeam Data Using Median Filtering[C]// IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, BC, Canada, 2001
    [21]
    王乐洋, 陈汉清. 多波束测深数据处理的抗差最小二乘配置迭代解法[J]. 测绘学报, 2017, 46(5): 658-665 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201705018.htm

    Wang Leyang, Chen Hanqing. Multi-beam Bathymetry Data Processing Using Iterative Algorithm of Robust Least Squares Collocation[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 658-665 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201705018.htm
    [22]
    黄贤源, 隋立芬, 翟国君, 等. 利用Bayes估计进行多波束测深异常数据探测[J]. 武汉大学学报·信息科学版, 2010, 35(2): 168-171 http://ch.whu.edu.cn/article/id/856

    Huang Xianyuan, Sui Lifen, Zhai Guojun, et al. Outliers Detection of Multi-Beam Data Based on Bayes Estimation[J]. Geomatics and Information Science of Wuhan University, 2010, 35(2): 168-171 http://ch.whu.edu.cn/article/id/856
    [23]
    阳凡林, 刘经南, 赵建虎. 多波束测深数据的异常检测和滤波[J]. 武汉大学学报·信息科学版, 2004, 29(1): 80-83 http://ch.whu.edu.cn/article/id/4687

    Yang Fanlin, Liu Jingnan, Zhao Jianhu. Detecting Outliers and Filtering Noises in Multi-beam Data[J]. Geomatics and Information Science of Wuhan University, 2004, 29(1): 80-83 http://ch.whu.edu.cn/article/id/4687
    [24]
    黄贤源, 翟国君, 隋立芬, 等. 最小二乘支持向量机在海洋测深异常值探测中的应用[J]. 武汉大学学报·信息科学版, 2010, 35(10): 1188-1191 http://ch.whu.edu.cn/article/id/1072

    Huang Xianyuan, Zhai Guojun, Sui Lifen, et al. Application of Least Square Support Vector Machine to Detecting Outliers of Multi-beam Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(10): 1188-1191 http://ch.whu.edu.cn/article/id/1072
    [25]
    Lecours V, Dolan M F J, Micallef A, et al. A Review of Marine Geomorphometry, the Quantitative Study of the Seafloor[J]. Hydrology and Earth System Sciences, 2016, 20(8): 3207-3244 doi: 10.5194/hess-20-3207-2016
    [26]
    Calder B R, Mayer L A. Automatic Processing of High-Rate, High-Density Multibeam Echosounder Data[J]. Geochemistry, Geophysics, Geosystems, 2003, 4(6): 1048
    [27]
    Vasquez M E. Tuning the CARIS Implementation of CUBE for Patagonian Waters[D]. Fredericton: University of New Brunswick, 2007
    [28]
    Park Y, Jung N D, Kim J S, et al. Performance Validation of Surface Filter Based on CUBE Algorithm for Eliminating Outlier in MultiBeam Echo Sounding[J/OL]. https://www.hydrographicsociety.org/documents/hydrographicsociety.org/downloads/ifhs_news_no_1_-_yosup_park_et_al.pdf, 2013
    [29]
    黄谟涛, 翟国君, 柴洪洲, 等. 检测多波束测深异常数据的CUBE算法模型解析[J]. 海洋测绘, 2011, 31(4): 1-4 doi: 10.3969/j.issn.1671-3044.2011.04.001

    Huang Motao, Zhai Guojun, Chai Hongzhou, et al. Analysis on the Mathematical Models of CUBE Algorithm for the Detection of Abnormal Data in Multibeam Echosounding[J]. Hydrographic Surveying and Charting, 2011, 31(4): 1-4 doi: 10.3969/j.issn.1671-3044.2011.04.001
    [30]
    赵荻能, 吴自银, 李家彪, 等. CUBE曲面滤波参数联合优选关键技术及应用[J]. 测绘学报, 2019, 48(2): 245-255 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201902015.htm

    Zhao Dineng, Wu Ziyin, Li Jiabiao, et al. The Key Technology and Application of Parameter Optimization Combined CUBE and Surface Filter[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(2): 245-255 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201902015.htm
    [31]
    Weil J. The Synthesis of Cloth Objects[J]. ACM SIGGRAPH Computer Graphics, 1986, 20(4): 49-54 doi: 10.1145/15886.15891
    [32]
    Long J, Burns K, Yang J. Cloth Modeling and Simulation: A Literature Survey[J]. Digital Human Modeling, 2011, DOI: 10.1007/978-3-642-21799-9_35
    [33]
    Zhang W M, Qi J B, Wan P, et al. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation[J]. Remote Sensing, 2016, 8(6): 501 doi: 10.3390/rs8060501
    [34]
    郭军, 刘胜旋, 关永贤, 等. 浅水多波束系统SONIC 2024在码头测深中的应用[J]. 测绘工程, 2016, 25(7): 46-50 https://www.cnki.com.cn/Article/CJFDTOTAL-CHGC201607010.htm

    Guo Jun, Liu Shengxuan, Guan Yongxian, et al. Application of SONIC 2024 Multibeam System to the Terminal Survey[J]. Engineering of Surveying and Mapping, 2016, 25(7): 46-50 https://www.cnki.com.cn/Article/CJFDTOTAL-CHGC201607010.htm
    [35]
    Calder B. Automatic Statistical Processing of Multibeam Echosounder Data[J]. International Hydrographic Review, 2003, 4(1): 53-68
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