Acta Geodaetica et Cartographica Sinica ›› 2018, Vol. 47 ›› Issue (1): 35-47.doi: 10.11947/j.AGCS.2018.20160565

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

ZHANG Zhiheng1,2, PENG Rencan1,2, HUANG Wenqian1,2,3, DONG Jian1,2, LIU Guohui4   

  1. 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)

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

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