利用聚类算法实现多波束水深数据异常值的自动识别与清理

doi:10.11947/j.AGCS.2022.20210113

测绘学报 ›› 2022, Vol. 51 ›› Issue (11): 2294-2302.doi: 10.11947/j.AGCS.2022.20210113

利用聚类算法实现多波束水深数据异常值的自动识别与清理

魏源^1,2, 金绍华¹, 李树军¹, 王磊^1,3, 边刚¹, 王沫¹

1. 海军大连舰艇学院军事海洋与测绘系, 辽宁大连 116018;
2. 91937部队, 浙江舟山 316002;
3. 92763部队, 辽宁大连 116018

收稿日期:2021-03-02 修回日期:2022-03-26 发布日期:2022-11-30
通讯作者: 金绍华 E-mail:jsh_1978@163.com
作者简介:魏源(1994—),男,硕士,研究方向为多波束水深测量数据处理。 E-mail: 383073307@qq.com
基金资助:
国家自然科学基金(41876103; 42071439)

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

WEI Yuan^1,2, JIN Shaohua¹, LI Shujun¹, WANG Lei^1,3, BIAN Gang¹, WANG Mo¹

1. Department of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, China;
2. Troops 91937, Zhoushan 316002, China;
3. Troops 92763, Dalian 116018, China

Received:2021-03-02 Revised:2022-03-26 Published:2022-11-30
Supported by:
The National Natural Science Foundation of China (Nos. 41876103; 42071439)

摘要/Abstract

摘要： 借鉴人工交互处理视角下多波束测深后视图中异常值的图像特征,利用密度聚类算法实现了无效数据的剔除及存疑数据的识别定位。利用聚类算法将多波束测深数据自动划分为可信、存疑和无效3类数据,可信数据保留,无效数据自动剔除,存疑数据人工判断。通过这种部分人工介入的异常值自动清理算法可较好地解决自动处理算法可信度低而人工交互处理效率低这一矛盾问题。实例计算表明:该算法在一定程度上提高了自动处理算法所得结果的可信度,对实现高可信度、高效率的多波束测深异常值清理具有重大意义。

关键词: 多波束测深, 聚类算法, 异常值, 自动清理

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

中图分类号:

P229

魏源, 金绍华, 李树军, 王磊, 边刚, 王沫. 利用聚类算法实现多波束水深数据异常值的自动识别与清理[J]. 测绘学报, 2022, 51(11): 2294-2302.

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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利用聚类算法实现多波束水深数据异常值的自动识别与清理

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

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