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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-06, Vol.11 (12), p.1418
Main Authors: Zheng, Zhaohui, Zheng, Hong, Ma, Yong, Fan, Fan, Ju, Jianping, Xu, Bichao, Lin, Mingyu, Cheng, Shuilin
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Detection
feature matching
Feedback
feedback unilateral grid-based clustering (FUGC)
Image registration
Linear transformations
Matching
mismatch
Pattern recognition
real-time
Registration
Remote sensing
Robustness (mathematics)
Run time (computers)
Screening
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11121418