考虑自然邻点影响域的多波束测深数据趋势面滤波改进算法

doi:10.11947/j.AGCS.2018.20160565

测绘学报 ›› 2018, Vol. 47 ›› Issue (1): 35-47.doi: 10.11947/j.AGCS.2018.20160565

考虑自然邻点影响域的多波束测深数据趋势面滤波改进算法

张志衡^1,2, 彭认灿^1,2, 黄文骞^1,2,3, 董箭^1,2, 刘国辉⁴

1. 海军大连舰艇学院海洋测绘系, 辽宁大连 116018;
2. 海军大连舰艇学院海洋测绘工程军队重点实验室, 辽宁大连 116018;
3. 地理信息工程国家重点实验室, 陕西西安 710054;
4. 海军出版社, 天津 300450

收稿日期:2016-11-08 修回日期:2017-10-11 出版日期:2018-01-20 发布日期:2018-02-05
通讯作者: 彭认灿 E-mail:pengrencan63@163.com
作者简介:张志衡(1990-),男,博士生,研究方向为数字海图和海洋地理信息工程。E-mail:zzhdl915@163.com
基金资助:
国家自然科学基金（41471380；41601498）；地理信息工程国家重点实验室开放研究基金（SKLGIE2016-M-4-1）

An Improved Algorithm of Tendency Surface Filtering in Multi-beam Bathymetric Data Considering the Natural Neighboring Points Influence Field

ZHANG Zhiheng^1,2, PENG Rencan^1,2, HUANG Wenqian^1,2,3, DONG Jian^1,2, LIU Guohui⁴

1. Department of Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, China;
2. Key Laboratory of Hydrographic Surveying and Mapping of PLA, Dalian Naval Academy, Dalian 116018, China;
3. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China;
4. Navy Press, Tianjin 300450, China

Received:2016-11-08 Revised:2017-10-11 Online:2018-01-20 Published:2018-02-05
Supported by:
The National Natural Science Foundation of China (Nos. 41471380;41601498);State Key Laboratory of Geo-information Engineering(No. SKLGIE2016-M-4-1)

摘要/Abstract

摘要： 针对传统趋势面滤波法在多波束测深数据粗差探测方面存在曲面拟合函数不确定、滤波不彻底以及部分水深点被不合理剔除的问题，通过引入散乱水深点局部最小范围——自然邻点影响域的概念，提出一种基于自然邻点影响域的多波束测深数据趋势面滤波改进算法。首先，通过对自然邻点影响域内的局部曲面进行分析，构造了影响域内特定局部坐标系下的统一曲面拟合函数；然后，利用该统一曲面拟合函数按照所提传递式迭代趋势面滤波法进行顺次迭代，逐步滤除影响正常水深点判定的粗差数据；最后，根据突变地形边界点在其相邻邻域地形内连续性不一致的特性，建立了面向突变地形边界点的判断准则。试验结果表明：该改进算法可适应不同复杂程度的海底地形，有效剔除多波束水深数据中的粗差点，同时对实际海底地形中的正常水深点和各种特殊水深点进行保留，显著提高了海底地形表达的精度。

关键词: 自然邻点, 影响域, 趋势面滤波, 粗差探测, 多波束测深数据

Abstract: Aiming at the problems of uncertain fitting function,incomplete filtering effects and some soundings unreasonable exclusion by applying tendency surface filtering algorithm in multi-beam bathymetric data,the paper brought forward the concept of natural neighboring points influence field,which means the minimum local area of an arbitrary point in scattered soundings,and erects an improved algorithm of tendency surface filtering based on natural neighboring points influence field. Firstly,the paper analyzed the local approximate surface of the natural neighboring points influence field,and constructed the unified surface fitting function in the specific local coordinate system for natural neighboring points influence field. Secondly,by using the unified surface fitting function,the iterative tendency surface filtering method has been erected to filter gross error data affecting the judgment of the normal points step by step. At last,according to the different continuity of the boundary point on the mutation terrain within the neighborhood adjacent terrain,a judgment criterion to the boundary point is established to reserve the boundary point. Some experiments were completed to prove the validity of the algorithm. The experiments show that the improved algorithm can adapt to the submarine topographies of varied complexities,eliminate the gross error points in the multi-beam bathymetric data and preserve the normal and special points of actual submarine topography. Therefore,the precision of submarine topography expression is significantly improved.

Key words: natural neighbor points, influence domain, tendency surface filtering, gross error detection, multi-beam bathymetric data

中图分类号:

P229

张志衡, 彭认灿, 黄文骞, 董箭, 刘国辉. 考虑自然邻点影响域的多波束测深数据趋势面滤波改进算法[J]. 测绘学报, 2018, 47(1): 35-47.

ZHANG Zhiheng, PENG Rencan, HUANG Wenqian, DONG Jian, LIU Guohui. An Improved Algorithm of Tendency Surface Filtering in Multi-beam Bathymetric Data Considering the Natural Neighboring Points Influence Field[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 35-47.

参考文献

[1] 刘雁春, 肖付民, 暴景阳, 等. 海道测量学概论[M]. 北京: 测绘出版社, 2006: 132-138. LIU Yanchun, XIAO Fumin, BAO Jingyang, et al. Introduction to Hydrogrophy[M]. Beijing: Surveying and Mapping Press, 2006: 132-138.
[2] 黄谟涛, 翟国君, 王瑞, 等. 海洋测量异常数据的检测[J]. 测绘学报, 1999, 28(3): 269-276. DOI: 10.3321/j.issn:1001-1595.1999.03.015. HUANG Motao, ZHAI Guojun, WANG Rui, et al. The Detection of Abnormal Data in Marine Survey[J]. Acta Geodaetica et Cartographica Sinica, 1999, 28(3): 269-277. DOI: 10.3321/j.issn:1001-1595.1999.03.015.
[3] 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. 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.
[4] 赵建虎, 刘经南. 多波束测深及图像数据处理[M]. 武汉: 武汉大学出版社, 2008: 205-210. ZHAO Jianhu, LIU Jingnan. Multi-beam Sounding and Image Data Processing[M]. Wuhan: Wuhan University Press, 2008: 205-210.
[5] WARE C, KNIGHT W, WELLS D. Memory Intensive Statistical Algorithms for Multibeam Bathymetric Data[J]. Computers & Geosciences, 1991, 17(7): 985-993.
[6] 朱庆, 李德仁. 多波束测深数据的误差分析与处理[J]. 武汉测绘科技大学学报, 1998, 23(1): 1-4, 46. ZHU Qing, LI Deren. Error Analysis and Processing of Multibeam Soundings[J]. Journal of Wuhan Technical University of Surveying and Mapping, 1998, 23(1): 1-4, 46.
[7] CALDER B R, MAYER L A. Automatic Processing of High-rate, High-density Multibeam Echosounder Data[J]. Geochemistry Geophysics Geosystems, 2003, 4(6): 1048.
[8] VÁSQUEZ M E. Tuning the CARIS Implementation of CUBE for Patagonian Waters[D]. New Brunswick: University of New Brunswick, 2007.
[9] MALLACE D, ROBERTSON P. Alternative Use of CUBE: How to Fit a Square Peg in a Round Hole[C]//Proceedings of US Hydrographic Conference. Norfolk, Virginia:[s.m.],2007.
[10] LCDR Aluizio Maciel de Oliveira Junior, CDR Izabel King Jeck. Multibeam Processing for Nautical Charts (Using CUBE and "Surface Filter" to Enhance Multibeam Processing)[J]. International Hydrographic Review, 2009(2): 61-71.
[11] 王德刚, 叶灿银. CUBE算法及其在多波束数据处理中的应用[J]. 海洋学研究, 2008, 26(2): 82-87. WAND Degang, YE Canyin. The Theory of CUBE Algorithm and Its Application in the Processing of Multi-beam Data[J]. Journal of Marine Sciences, 2008, 26(2): 82-87.
[12] 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. 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.
[13] 李新娜, 归庆明, 许阿裴. 基于识别变量的粗差探测Bayes方法[J]. 测绘学报, 2008, 37(3): 355-360, 366. DOI: 10.3321/j.issn:1001-1595.2008.03.015. LI Xinna, GUI Qingming, XU Apei. Bayesian Method for Detection of Gross Errors Based on Classification Variables[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(3): 355-360, 366. DOI: 10.3321/j.issn:1001-1595.2008.03.015.
[14] 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. 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.
[15] 黄贤源, 翟国君, 黄谟涛, 等. 利用水深不确定度探测测深异常值的方法[J]. 测绘科学技术学报, 2011, 28(1): 70-74, 78. HUANG Xianyuan, ZHAI Guojun, HUANG Motao, et al. The Approach on Detecting Outliers of Multi-beam Data by Uncertainty[J]. Journal of Geomatics Science and Technology, 2011, 28(1): 70-74, 78.
[16] 董江, 任立生. 基于趋势面的多波束测深数据滤波方法[J]. 海洋测绘, 2007, 27(6): 25-28. DONG Jiang, REN Lisheng. Filter of MBS Sounding Data Based on Trend Surface[J]. Hydrographic Surveying and Charting, 2007, 27(6): 25-28.
[17] 王海栋, 柴洪洲, 宋国大, 等. 多波束测深趋势面系数的主成分估计[J]. 海洋测绘, 2009, 29(5): 5-7. WANG Haidong, CHAI Hongzhou, SONG Guoda, et al. Principal Components Estimation of Trend Surface Coefficients in Multibeam Bathymetry[J]. Hydrographic Surveying and Charting, 2009, 29(5): 5-7.
[18] 王海栋, 柴洪洲, 翟天增, 等. 多波束测深异常的两种趋势面检测算法比较[J]. 海洋通报, 2010, 29(2): 182-186. WANG Haidong, CHAI Hongzhou, ZHAI Tianzeng, et al. Comparison of Two Trend Surface Detection Algorithms of Multibeam Bathymetry Outlier[J]. Marine Science Bulletin, 2010, 29(2): 182-186.
[19] 李志林, 朱庆. 数字高程模型[M]. 2版. 武汉: 武汉大学出版社, 2008: 17-19, 112-133. LI Zhilin, ZHU Qing. Digital Elevation Model[M]. 2nd ed. Wuhan: Wuhan University Press, 2008: 17-19, 112-133.
[20] YANG Xunnian, WANG Guozhao. Planar Point Set Fairing and Fitting by Arc Splines[J]. Computer-Aided Design, 2001, 33(1): 35-43.
[21] 陈曦. 反求工程中基于点云的特征挖掘技术研究[D]. 杭州: 浙江大学, 2005: 38-39. CHEN Xi. Study on Feature Mining Technology Based on Point Cloud in Reverse Engineering[D]. Hangzhou: Zhejiang University, 2005: 38-39.
[22] 汤国安, 刘学军, 闾国年. 数字高程模型及地学分析的原理与方法[M]. 北京: 科学出版社, 2005: 63-65. TANG Guoan, LIU Xuejun, LÜ Guonian. Principle and Method of Digital Elevation Model and Geological Analysis[M]. Beijing: Science Press, 2005: 63-65.
[23] 王幼宁, 刘继志. 微分几何讲义[M]. 北京: 北京师范大学出版社, 2011: 116-117. WANG Youning, LIU Jizhi. Differential Geometry Notes[M]. Beijing: Beijing Normal University Press, 2011: 116-117.
[24] 陈军, 赵仁亮, 乔朝飞. 基于Voronoi图的GIS空间分析研究[J]. 武汉大学学报(信息科学版), 2003, 28(S1): 32-37. CHEN Jun, ZHAO Renliang, QIAO Chaofei. Voronoi Diagram-based GIS Spatial Analysis[J]. Geomatics and Information Science of Wuhan University, 2003, 28(S1): 32-37.
[25] 董箭, 彭认灿, 郑义东, 等. 局部动态最优Voronoi图的NNI算法及其在格网数字水深模型中的应用[J]. 测绘学报, 2013, 42(2): 284-289, 303. DONG Jian, PENG Rencan, ZHENG Yidong, et al. An algorithm of Natural Neighbor Interpolation Based on local Dynamic Optimal Voronoi Diagram and Its Application in Grid Digital Depth Model[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2): 284-289, 303.
[26] 郭艳军, 潘懋, 燕飞, 等. 自然邻点插值方法在三维地质建模中的应用[J]. 解放军理工大学学报(自然科学版), 2009, 10(6): 650-655. GUO Yanjun, PAN Mao, YAN Fei, et al. Application of Natural Neighbor Interpolation Method in Three-Dimensional Geological Modeling[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2009, 10(6): 650-655.
[27] 吴英姿. 多波束测深系统地形跟踪与数据处理技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2001: 59-79. WU Yingzi. A Study on Multi-Beam Sounding System Seafloor Tracking & Data Processing Techniques[D]. Harbin: Harbin Engineering University, 2001: 59-79.
[28] 蔡永昌, 朱合华. 基于局部搜索算法的自然邻接点方法[J]. 力学学报, 2004, 36(5): 623-628. CAI Yongchang, ZHU Hehua. Natural Neighbour Method Based on the Algorithm of Local Search[J]. Acta Mechanica Sinica, 2004, 36(5): 623-628.
[29] 董箭, 彭认灿, 郑义东. 利用局部动态最优Delaunay三角网改进逐点内插算法[J]. 武汉大学学报(信息科学版), 2013, 38(5): 613-617. DONG Jian, PENG Rencan, ZHENG Yidong. An Improved Algorithm of Point-by-point Interpolation by Using Local Dynamic Optimal Delaunay Triangulation Network[J]. Geomatics and Information Science of Wuhan University, 2013, 38(5): 613-617.
[30] 吕志平, 魏子卿, 李军, 等. 我国CGCS2000高精度坐标转换格网模型的建立[J]. 测绘学报, 2013, 42(6): 791-797. LÜ Zhiping, WEI Ziqing, LI Jun, et al. The Establishment of High Precise Coordinate Transformation Grid Model of CGCS2000[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(6): 791-797.

考虑自然邻点影响域的多波束测深数据趋势面滤波改进算法

An Improved Algorithm of Tendency Surface Filtering in Multi-beam Bathymetric Data Considering the Natural Neighboring Points Influence Field

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	颜金彪, 吴波, 彭馨. 顾及Gestalt邻近与简化原则的平面点集形状重建[J]. 测绘学报, 2020, 49(11): 1485-1496.
[2]	闫广峰, 岑敏仪. L₁范数探测粗差失效的观测量识别方法[J]. 测绘学报, 2019, 48(11): 1430-1438.
[3]	张亚彬, 王利, 范丽红, 曲轩宇. 组合粗差探测的MHSS ARAIM算法[J]. 测绘学报, 2019, 48(1): 9-17.
[4]	郑凯, 刘站科, 肖学年, 张小红. 航空重力GPS测速多粗差探测方法[J]. 测绘学报, 2016, 45(6): 663-669.
[5]	仝海波张国柱. 改进M估计的抗多个粗差定位解算方法[J]. 测绘学报, 2014, 43(4): 366-371.
[6]	孙文王庆宾朱志大. 联合多代卫星测高数据构建中国近海及邻域海平面异常序列[J]. , 2013, 42(4): 0-0.
[7]	孙海燕,黄华兵,王喜娜. 多维平差问题粗差的局部分析法[J]. 测绘学报, 2012, 41(1): 54-0.
[8]	李博峰. 基于等效残差积探测粗差的方差-协方差分量估计[J]. 测绘学报, 2011, 40(1): 10-14.
[9]	黄贤源,翟国君,隋立芬,黄谟涛,欧阳永忠,柴洪洲. LS-SVM算法中优化训练样本对测深异常值剔除的影响[J]. 测绘学报, 2011, 40(1): 22-27.
[10]	徐天河,贺凯飞. 移动开窗检验法及其在GOCE数据粗差探测中的应用[J]. 测绘学报, 2009, 38(5): 0-414.