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Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

School of Electronic Information, Wuhan University, Wuhan 430079, China
Department of Public Courses, Wuhan Railway Vocational College of Technology, Wuhan 430205, China
School of Mechanical & Electrical and Information Engineering, Hubei Business College, Wuhan 430000, China
Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430000, China
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430000, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1418;
Received: 27 April 2019 / Revised: 10 June 2019 / Accepted: 10 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
PDF [20923 KB, uploaded 14 June 2019]


In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement. View Full-Text
Keywords: feature matching; feedback unilateral grid-based clustering (FUGC); real-time; remote sensing; mismatch feature matching; feedback unilateral grid-based clustering (FUGC); real-time; remote sensing; mismatch

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Zheng, Z.; Zheng, H.; Ma, Y.; Fan, F.; Ju, J.; Xu, B.; Lin, M.; Cheng, S. Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration. Remote Sens. 2019, 11, 1418.

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