基于流线距离聚类的海洋数据向量场可视化

doi:10.11896/jsjkx.220300284

计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220300284-7.doi: 10.11896/jsjkx.220300284

基于流线距离聚类的海洋数据向量场可视化

王朕^1,2, 杨政威¹, 高顺起¹, 张磊¹

1 天津财经大学理工学院天津 300222;
2 自然资源部海洋信息技术创新中心天津 300171

出版日期:2023-06-10 发布日期:2023-06-12
通讯作者: 王朕(wangzhen@tjufe.edu.cn)
基金资助:
自然资源部海洋信息技术创新中心开放基金(201906);国家自然科学基金(62172294)

Visualization of Ocean Data Vector Field Based on Streamline Distance Clustering

WANG Zhen^1,2, YANG Zhengwei¹, GAO Shunqi¹, ZHANG Lei¹

1 School of Science and Technology,Tianjin University of Finance and Economics,Tianjin 300222,China;
2 Marine Information Technology Innovation Center,Ministry of Natural Resources,Tianjin 300171,China

Online:2023-06-10 Published:2023-06-12
About author:WANG Zhen,born in 1981,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include multi-dimensional reconstruction and visualization for application in computed tomography and computer vision.
Supported by:
Open Fund of the Marine Information Technology Innovation Center of the Ministry of Natural Resources(201906) and National Natural Science Foundation of China(62172294).

摘要/Abstract

摘要： 流线可视化是海洋向量场可视化的重要研究对象,其中流线种子点的数量和位置设定是基础,而生成流线的准确聚类以及类内代表性流线的选取是消除冗余流线造成的视觉混乱和遮挡问题的关键。文中提出将PDM距离作为流线聚类的相似性度量值,在流线端点聚类的基础上再进行流线精细聚类,有效解决端点聚类结果不准确的问题,提升了流线聚类的准确性。通过排序聚类后类内流线对的PDM距离值,提取中线和边界线进行流线重绘,减少了流线遮挡和杂乱现象。针对基于距离的流线聚类计算量大的问题,提出了MDS算法以提升计算速度。此外,采取临界点检测算法减少了流线生成过程中耗时的漩涡生成,进一步提升了计算速度。使用中国沿海的海洋流场数据进行实验,验证了算法的有效性和优越性,流线绘制效果良好。

关键词: 聚类, 向量场可视化, 流线距离

Abstract: Streamlines visualization is an important research object of ocean vector field visualization,in which the setting of number and location for streamlines seed points is the basis.The key to the problem,how to eliminate the visual confusion and occlusion caused by multiple streamlines,is the accurate clustering of generated streamlines and the selection of appropriate represen-tative streamlines within the clustering.In this paper,the PDM distance is proposed to be the similarity measurement,then the fine streamline clustering is implemented after once endpoints streamline clustering.The proposed method effectively solves the problem of inaccurate endpoint clustering results and improves the accuracy of streamline clustering.After sort of all the PDM distance within each clustering,we extract midline and two boundary lines to redraw the streamlines,so as to reduce the pheno-menon of visual confusion and occlusion caused by multiple streamlines.For the problem of huge amount of calculation,the MDS algorithm is proposed to reduce dimension and accelerate computing speed.In addition,in order to further accelerate the calculation speed,the critical point detection algorithm is adopted to reduce the time-consuming vortex generation during the process ofstreamline generation.The effectiveness and superiority of our proposed method are verified by using ocean flow field data from China coast,and the drawing effect of streamline is good.

Key words: Clustering, Vector field visualization, Streamline distance

中图分类号:

TP391

王朕, 杨政威, 高顺起, 张磊. 基于流线距离聚类的海洋数据向量场可视化[J]. 计算机科学, 2023, 50(6A): 220300284-7. https://doi.org/10.11896/jsjkx.220300284

WANG Zhen, YANG Zhengwei, GAO Shunqi, ZHANG Lei. Visualization of Ocean Data Vector Field Based on Streamline Distance Clustering[J]. Computer Science, 2023, 50(6A): 220300284-7. https://doi.org/10.11896/jsjkx.220300284

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基于流线距离聚类的海洋数据向量场可视化

Visualization of Ocean Data Vector Field Based on Streamline Distance Clustering

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