Automatic recognition and cleaning of outliers in multi-beam bathymetric data with clustering algorithm

doi:10.11947/j.AGCS.2022.20210113

Abstract

Abstract: Eliminating the invalid data and locating the doubtful data are realized by using the algorithm of density clustering, by referring to the image features of outliers in the multi-beam rear view from the perspective of manual interactive processing.The multi-beam bathymetric data are automatically divided into three types: credible data, doubtful data and invalid data by clustering algorithm. The reliable data are retained, invalid data are automatically eliminated, and the doubtful data are judged manually.This kind of automatic outlier cleaning algorithm with partial manual intervention can solve the contradiction between low reliability of automatic processing algorithm and low efficiency of manual interactive processing.An example shows that the algorithm improves the reliability of the results obtained from the automatic processing algorithm to a certain extent, and also,the algorithm is of great significance for the realization of high reliability and high efficiency cleaning of outliers in multi-beam bathymetric data.

Key words: multi-beam bathymetry, clustering algorithm, outliers, automatic cleaning

CLC Number:

P229

WEI Yuan, JIN Shaohua, LI Shujun, WANG Lei, BIAN Gang, WANG Mo. Automatic recognition and cleaning of outliers in multi-beam bathymetric data with clustering algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11): 2294-2302.

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