A probability-based multi-measure feature matching method in map conflation

Xiaohua Tong, Wen Zhong Shi, Susu Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


This paper presents a probability-based multi-measure feature matching method in map conflation. Feature matching is used to determine the corresponding features in different datasets that represent analogous entities in the real world. In the proposed method, a total matching probability is computed by the weighted average of multiple measures, including positional measure, shape measure, directional measure and topological measure. The matching strategies for point features, linear features and areal features are also provided. The proposed method is implemented in a prototype for matching features from two different data sources, and is compared with traditional methods. The results demonstrate not only the practicability of using the proposed method to resolve feature matching issues in map conflation, but also its advantages compared with traditional methods in terms of matching effects.
Original languageEnglish
Pages (from-to)5453-5472
Number of pages20
JournalInternational Journal of Remote Sensing
Issue number20
Publication statusPublished - 1 Jan 2009

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


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