地球信息科学学报 ›› 2013, Vol. 15 ›› Issue (5): 635-642.doi: 10.3724/SP.J.1047.2013.00635

• 地球信息科学理论与方法 • 上一篇    下一篇

空间面群目标几何相似度计算模型

刘涛, 闫浩文   

  1. 兰州交通大学测绘与地理信息学院, 兰州 730070
  • 收稿日期:2013-03-26 修回日期:2013-05-27 出版日期:2013-09-29 发布日期:2013-09-29
  • 通讯作者: 刘涛,E-mail:ltaochina@foxmail.com E-mail:ltaochina@foxmail.com
  • 作者简介:刘涛(1981-),男,湖北随州人,博士,副教授,研究方向为空间关系。E-mail:ltaochina@foxmail.com
  • 基金资助:

    国家自然科学基金项目(41201476);兰州交通大学青年科技基金项目(2012001)。

Geometry Similarity Assessment Model of Spatial Polygon Groups

LIU Tao, YAN Haowen   

  1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Received:2013-03-26 Revised:2013-05-27 Online:2013-09-29 Published:2013-09-29

摘要:

本文针对空间面群目标提出了一种几何相似度计算模型。首先,利用拓扑关系概念领域图定义了面群之间的拓扑关系相似度;然后,对不同类型的面状目标选用合适的“降维”方法处理为“线群”目标,利用方向均值定义线群之间的方向关系即面群目标的方向相似度,以及利用“环形方差”定义线群目标之间的距离关系即面群目标的距离相似度。最后,结合面群的长度和平均长度、面积和平均面积,面密度及紧致度,建立了面群目标几何相似度计算模型,以对面群目标相似度进行整体度量。该模型综合考虑了空间面群目标的几何特征和空间关系特征,并对其作了适当的权重分配。从时间邻近度和尺度邻近度角度,本文设计了2个实验,结果表明,相似度计算结果与地物特征比较一致,符合人们的直观空间认知。

关键词: 空间相似性, 几何相似度, 空间面群, 空间关系

Abstract:

As a kind of spatial relationship, spatial similarity relationship is still in its initial research stage. Research works of similarity assessment of spatial group objects will improve spatial relationship's theory, deepen spatial cognition and raise the level of spatial data intelligent and automated handling. Focus on the spatial polygon groups, this paper proposed a novel computational model of geometry similarity measurement between spatial polygon groups. The conceptual neighborhood network of topological relationship was utilized to define topological relationship similarity between polygon groups. A suitable "dimensionality reduction" approach was utilized to process different polygon objects into line group. Then, the directional mean was utilized to define direction relationship between line groups, namely direction similarity of polygon groups. Finally, the circular variance was utilized to define distance relationship between line groups, namely distance similarity of polygon groups. Combined with the length, average length, area, average area, density and compactness of polygon groups, the final step established a computational model of similarity to comprehensively measure the geometry similarity of polygon groups. The computation model considered both geometry features and spatial relationship features of polygon groups, and a proper weights distribution was taking into account at the same time. Two experiments were taken out to verify the model. The experimental results showed that the computed similarity is consistent with ground object features and intuitive cognition of human beings.

Key words: geometry similarity, spatial relationship, spatial similarity, polygon group