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

作　　者：魏源 金绍华 李树军[1] 王磊[1,3] 边刚 王沫 WEI Yuan;JIN Shaohua;LI Shujun;WANG Lei;BIAN Gang;WANG Mo(Department of Military Oceanography and Hydrography and Cartography,Dalian Naval Academy,Dalian 116018,China;Troops 91937,Zhoushan 316002,China;Troops 92763,Dalian 116018,China)

机构地区：[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

出　　处：《测绘学报》2022年第11期2294-2302,共9页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(41876103,42071439)。

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。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.

关 键 词：多波束测深 聚类算法 异常值 自动清理 

分 类 号：P229[天文地球—大地测量学与测量工程]