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Remote Sens. 2017, 9(12), 1249; https://doi.org/10.3390/rs9121249

Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Satellite Surveying and Mapping Application Center, NASG, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Received: 22 October 2017 / Revised: 29 November 2017 / Accepted: 30 November 2017 / Published: 1 December 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical satellite imagery. The proposed method includes three steps: feature extraction, initial matching, and matching propagation. Firstly, a uniform robust scale invariant feature transform (UR-SIFT) detector was used to extract adequate and well-distributed feature points. Secondly, initial matching was conducted based on the Euclidean distance to obtain a few correct matches and the initial projective transformation between the image pair. Finally, two matching strategies were used to propagate matches and produce more reliable matching results. By using the geometric relationship between the image pair, geometric correspondence matching found more matches than the initial UR-SIFT feature points. Further probability relaxation matching propagated some new matches around the initial UR-SIFT feature points. Comprehensive experiments on Chinese ZY3 and GaoFen (GF) satellite images revealed that the proposed algorithm performs well in terms of the number of correct matches, correct matching rate, spatial distribution, and matching accuracy, compared to the standard UR-SIFT and triangulation-based propagation method. View Full-Text
Keywords: multi-source optical satellite imagery; image matching; propagation matching; geometric correspondence matching; probability relaxation matching multi-source optical satellite imagery; image matching; propagation matching; geometric correspondence matching; probability relaxation matching
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Liu, Y.; Mo, F.; Tao, P. Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach. Remote Sens. 2017, 9, 1249.

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