地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (2): 147-156.doi: 10.12082/dqxxkx.2019.180298

• 地球信息科学理论与方法 • 上一篇    下一篇

面向区域增量更新的等高线群混合相似性度量模型

郭文月1(), 刘海砚1, 孙群1, 余岸竹1, 陈焕新2   

  1. 1. 信息工程大学,郑州 450001
    2. 96633部队,北京 100096
  • 收稿日期:2018-06-25 修回日期:2018-12-08 出版日期:2019-02-20 发布日期:2019-01-30
  • 作者简介:

    作者简介:郭文月(1990-),女,辽宁辽阳人,博士生,研究方向为数字地图制图与遥感影像辅助更新。E-mail: guowyer@163.com

  • 基金资助:
    国家自然科学基金项目(41501446、41801388)

A Contour Group Mixed Similarity Measurement Model for Region Incremental Updating

Wenyue GUO1,*(), Haiyan LIU1, Qun SUN1, Anzhu YU1, Huanxin CHEN2   

  1. 1. Information Engineering University, Zhengzhou 450001, China
    2. 96633 Troops, Beijing 100096, China
  • Received:2018-06-25 Revised:2018-12-08 Online:2019-02-20 Published:2019-01-30
  • Contact: Wenyue GUO E-mail:guowyer@163.com
  • Supported by:
    National Natural Science Foundation of China, No.41501446, 41801338

摘要:

等高线是一种以曲线群簇展现地表起伏形态的表达方式,多源等高线数据之间的相似度能够反映地形地貌的变化程度,因此等高线群的相似性度量是地形图更新、多源数据融合及制图综合领域的关键环节之一。当前的等高线相似性度量方法主要基于要素的单一拓扑特征或几何特征,由于地理空间数据的复杂性和地理要素变化的多样性,这种通过计量多源数据数据单一特征之间的相似与差异程度的方法并不能完整表达多源数据之间的异同,在变化复杂区域、图幅边界区域以及等高线分布密集区域会导致不一致问题。因此,本文引入空间相似度理论,综合探讨了等高线群的相似性层次结构;研究了拓扑特征和几何特征在等高线群相似性度量中的关系和作用机理,构建了等高线群相似性层次结构;讨论了其中各个影响要素的相互关系和相似性度量方法,提出了一种基于拓扑特征和几何特征的区域等高线群混合相似性度量模型,并利用层次分析方法求解各级相似元的权重系数。通过模拟实验和真实数据实验对本文方法的可靠性和有效性进行验证,结果表明:本文提出的等高线群混合相似性度量模型能够定量描述不同尺度不同来源等高线群之间的相似与差异程度,并具有较好的有效性和可靠性;根据本文的混合相似性度量结果和更新阈值之间的关系,对满足更新要求的变化区域实施局部更新,且精度检验表明论文方法能够为等高线数据的更新应用提供可靠依据。

关键词: 等高线群, 相似性度量, 拓扑相似度, 几何相似度, 层次分析法, 地形局部更新

Abstract:

Contour line is used to express surface information through curve cluster. The degree of topography change can be reflected based on the similarity between multi-source contour data. Therefore, the similarity measurement of contour groups is an essential step in the map partial renewal, multi-source data merging and cartographic generalization of topographic maps. Previous measurement methods are mainly based on measuring the single topological feature or geometric feature. Due to the complexity of geospatial data and the diversity of geographic elements, the existing methods may not completely reflect the similarities and differences between multi-source data, which may cause inconsistencies in areas with intensive contours or extreme terrain changes and map boundaries in incremental renewal application. For this reason, the spatial similarity theory is introduced and the similarity structure of contour group is built. Through analyzing the relationship and mechanism of the topological relations and geometric features, the hierarchical structure of contour group similarity is constructed, and the mutual relationship and similarity measurement methods of each influencing factor are discussed. Based on the hierarchical structure, a mixed similarity measure model using topological relation tree and geometric similarity measures is proposed. In the mixed measure model, the weight coefficients are calculated based on the analytic hierarchy process. Simulated and real datasets experiments are used to verify the reliability and validity of the similarity measure model proposed in this paper. The experimental results show that: (1) The mixed similarity measure model can quantitatively describe the similarities and differences between contour data from different scales and sources. (2) According to the relationship between the mixed similarity measure results and the update thresholds, partial renewing is applied to the changing areas that meet the update requirements. The accuracy test shows that the proposed similarity measure method has a good validity and reliability.

Key words: contour groups, similarity measure, topological similarity, geometric similarity, analytic hierarchy process, map partial renewing