计算机工程 ›› 2018, Vol. 44 ›› Issue (9): 52-58.doi: 10.19678/j.issn.1000-3428.0049799

• 先进计算与数据处理 • 上一篇    下一篇

基于趋势面与SSIM的时空数据相似度算法

李建勋,佟瑞,张永进,唐子豪   

  1. 西安理工大学 经济与管理学院,西安 710054
  • 收稿日期:2017-12-21 出版日期:2018-09-15 发布日期:2018-09-15
  • 作者简介:李建勋(1977—),男,副教授、博士,主研方向为时空数据处理、决策支持系统;佟瑞,硕士研究生;张永进,教授、博士;唐子豪,硕士研究生。
  • 基金项目:

    陕西省自然科学基础研究计划项目(2015JM5198)。

Similarity Algorithm of Spatio-temporal Data Based on Trend Surface and SSIM

LI Jianxun,TONG Rui,ZHANG Yongjin,TANG Zihao   

  1. School of Economics and Management,Xi’an University of Technology,Xi’an 710054,China
  • Received:2017-12-21 Online:2018-09-15 Published:2018-09-15

摘要:

针对空间位置固定而属性值趋势变化的时空数据相似度评判问题,在采用Biharmonic样条建立趋势面的基础上,提出一种新的时空数据相似度算法。利用网格抽取和色阶映射形成趋势面图像,将时空数据趋势状态表征为图像的结构信息,以趋势面图像之间的相似度来表征时空数据的相似度,并通过结构相似性给出时空数据结构相似度评价方案,实现时间维度的相似度合成,避免传统依靠向量空间分析的片面性,为一定时间窗口下的时空数据相似度分析提供解决方案。实验结果表明,该算法能够有效刻画时空数据所蕴含的趋势信息,提高该类时空数据相似度算法的适用性。

关键词: 时空数据, 趋势面, 结构相似度算法, Biharmonic样条, Green函数

Abstract:

Aiming at the problem of spatio-temporal data similarity evaluation with fixed spatial position and trend of attribute value trend,based on the Biharmonic spline to establish the trend surface,a new spatio-temporal data similarity algorithm is proposed.The trend surface image is formed by mesh extraction and color gradation mapping,and the trend state of the spatio-temporal data is characterized as the structural information of the image.The similarity of spatio-temporal data is characterized by the similarity between the trend surface images,and through Structural Similarity(SSIM),the similarity evaluation scheme of spatio-temporal data structure is given,and the similarity synthesis of time dimension is implemented to avoid the one-sidedness of traditional relying on vector space analysis,and a solution for spatio-temporal data similarity analysis is provided under a certain time window.Experimental results show that the algorithm can effectively describe the trend information contained in spatio-temporal data and improve the applicability of such spatio-temporal data similarity algorithm.

Key words: spatio-temporal data, trend surface, Structural Similarity(SSIM) algorithm, Biharmonic spline, Green function

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