Abstract: Intensity-or gradient-based similarity measurement leads to outliers caused by similar objects.To address this problem,a novel feature matching method using topology similarity constraints is proposed.Using Euclidean distance to measure similarity,similar contents are significantly closer than dissimilar contents.K nearest neighbor algorithm is proposed to find matched candidates which have significantly smaller distances.With plane-to-plane homography,matched features are divided into several feature-sets by planes.Then all feature-sets are hierarchically triangulated.With five topology similarity constraints,outliers are removed,and m∶n-matches are reduced to 1∶1-matches.The experimental results show that the proposed method successfully removed the outliers except the stray points.