Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images

被引:63
作者
Jiang, Xingyu [1 ]
Jiang, Junjun [3 ]
Fan, Aoxiang [2 ]
Wang, Zhongyuan [4 ]
Ma, Jiayi [1 ]
机构
[1] Wuhan Univ, Elect Informat Sch, Wuhan 430072, Hubei, Peoples R China
[2] Wuhan Univ, Elect Informat Sch, Multispectral Vis Proc Lab, Wuhan 430072, Hubei, Peoples R China
[3] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
[4] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp, Wuhan 430072, Hubei, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2019年 / 57卷 / 09期
基金
中国国家自然科学基金;
关键词
Feature matching; local structure; multiscale; nonrigid; ranking list; REGISTRATION; OPTIMIZATION;
D O I
10.1109/TGRS.2019.2906183
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
As a fundamental and important task in many applications of remote sensing and photogrammetry, feature matching tries to seek correspondences between the two feature sets extracted from an image pair of the same object or scene. This paper focuses on eliminating mismatches from a set of putative feature correspondences constructed according to the similarity of existing well-designed feature descriptors. Considering the stable local topological relationship of the potential true correspondences, we propose a simple yet efficient method named multiscale Top K Rank Preservation (mTopKRP) for robust feature matching. To this end, we first search the K-nearest neighbors of each feature point and generate a ranking list accordingly. Then we design a metric based on the weighted Spearman's footrule distance to describe the similarity of two ranking lists specifically for the matching problem. We build a mathematical optimization model and derive its closed-form solution, enabling our method to establish reliable correspondences in linearithmic time complexity, which requires only tens of milliseconds to handle over 1000 putative matches. We also introduce a multiscale strategy for neighborhood construction, which increases the robustness of our method and can deal with different types of degradation, even when the image pair suffers from a large scale change, rotation, nonrigid deformation, or a large number of mismatches. Extensive experiments on several representative remote sensing image data sets demonstrate the superiority of our method over state of the art.
引用
收藏
页码:6462 / 6472
页数:11
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