Point-matching algorithm based on local neighborhood information for remote sensing image registration

Yue Wu; Wenping Ma; Jun Zhang; Yong Zhong; Liang Liu

doi:10.1117/1.JRS.12.016002

2 January 2018 Point-matching algorithm based on local neighborhood information for remote sensing image registration

Yue Wu, Wenping Ma, Jun Zhang, Yong Zhong, Liang Liu

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Natural Science Foundation of China, National Natural Science Foundation of China, Major Research Plan of the National Natural Science Foundation of China, National Basic Research Program (973 Program) of China, National Basic Research Program (973 Program), Doctoral Program of Higher Education of China, Fundamental Research Funds for the Central Universities

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016002 (January 2018). https://doi.org/10.1117/1.JRS.12.016002

Abstract

Robust feature point matching is a critical procedure in feature-based remote sensing image registration. A point-matching algorithm is proposed, which uses the similarity of local neighborhood information of point features. First, we establish a set of initial correspondences. Then we focus on removing incorrect correspondences (outliers) by local neighborhood information and increasing the number of correct correspondences (inliers). Finally, a global structure constraint is constructed for each remaining correct correspondence to increase the number of inliers and raise the correct rate simultaneously. Experimental results compared with three state-of-the-art methods illustrate that the proposed method can find more correct matching points with high accuracy.

Citation Download Citation

Yue Wu, Wenping Ma, Jun Zhang, Yong Zhong, and Liang Liu "Point-matching algorithm based on local neighborhood information for remote sensing image registration," Journal of Applied Remote Sensing 12(1), 016002 (2 January 2018). https://doi.org/10.1117/1.JRS.12.016002

Received: 28 July 2017; Accepted: 15 November 2017; Published: 2 January 2018

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