Feature matching algorithm based on spatial similarity

Wenjing Tang; Yanling Hao; Yuxin Zhao; Ning Li

doi:10.1117/12.813204

7 November 2008 Feature matching algorithm based on spatial similarity

Wenjing Tang, Yanling Hao, Yuxin Zhao, Ning Li

Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 714704 (2008) https://doi.org/10.1117/12.813204
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

The disparities of features that represent the same real world entities from disparate sources usually occur, thus the identification or matching of features is crutial to the map conflation. Motivated by the idea of identifying the same entities through integrating known information by eyes, the feature matching algorithm based on spatial similarity is proposed in this paper. Total similarity is obtained by integrating positional similarity, shape similarity and size similarity with a weighted average algorithm, then the matching entities is achieved according to the maximum total similarity. The matching of areal features is analyzed in detail. Regarding the areal feature as a whole, the proposed algorithm identifies the same areal features by their shape-center points in order to calculate their positional similarity, and shape similarity is given by the function of describing the shape, which ensures its precision not be affected by interferes and avoids the loss of shape information, furthermore the size of areal features is measured by their covered areas. Test results show the stability and reliability of the proposed algorithm, and its precision and recall are higher than other matching algorithm.

Citation Download Citation

Wenjing Tang, Yanling Hao, Yuxin Zhao, and Ning Li "Feature matching algorithm based on spatial similarity", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 714704 (7 November 2008); https://doi.org/10.1117/12.813204

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