中误差和邻近关系的多尺度面实体匹配算法研究

测绘学报

中误差和邻近关系的多尺度面实体匹配算法研究

刘坡¹,张宇¹,龚建华²

1. 中国科学院遥感应用研究所
2. 中国科学院遥感应用研究所遥感科学国家重点实验室

收稿日期:2012-11-19 修回日期:2013-12-06 出版日期:2014-04-20 发布日期:2013-12-19
通讯作者: 刘坡
基金资助:
科技支撑计划;深圳市科技研发资金项目;国家自然科学基金面上基金;中国博士后科学基金面上资助

Multi-Scale Areal Feature Matching Approach Based On Root Mean Square Error And Neighbouring Relation

Received:2012-11-19 Revised:2013-12-06 Online:2014-04-20 Published:2013-12-19

摘要/Abstract

摘要：

地图目标匹配作为空间数据整合和更新的一个不可缺少的过程，有重要的研究意义。中误差是一种衡量地图精度和质量的数值指标,其范围作为制图和综合的重要的标准之一，常用其大小评价空间数据的质量,不同比例尺或来源的地图数据均有不同的中误差大小和阈值。面状要素在很多地图中占有很大的比例，本文将中误差引入面实体匹配的过程，结合相邻面实体邻近聚集算法，提出一种基于中误差和邻近关系的面实体匹配算法,可以有效解决多尺度空间数据匹配的阈值大小和多对多关系难确定的问题，实验结果表明该方法具有良好的稳定性和可靠性。

关键词: 多尺度, 面, 中误差, 邻近关系, 数据匹配

Abstract:

Map object matching as an indispensable process in spatial data integration and updating has important researching significance. Root Mean Square Error (RMSE) as a numerical indicator that measures map accuracy and quality , its range is as one of the important standards in mapping and cartography generalization, and commonly used to balance the quality of spatial data, The data of different scale or sources are varying in size and threshold of the RMSE. Polygons occupy a much proportion in a map, this paper introduces the RMSE in the process of areal feature matching, combined with the clustering algorithm of adjacent Polygon to match areal feature. It can effectively solve the problems for dertermining the threshold and many-to-many relationship in the multi-scale spatial areal feature matching, the experimental results show that this method is stability and reliability.

Key words: Multi-scale, Areal Feature, Root Mean Square Error, Neighbouring Relation, Data Matching

中图分类号:

刘坡张宇龚建华. 中误差和邻近关系的多尺度面实体匹配算法研究[J]. 测绘学报.

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