Next Article in Journal
Ionospheric Peak Parameters Retrieved from FY-3C Radio Occultation: A Statistical Comparison with Measurements from COSMIC RO and Digisondes Over the Globe
Next Article in Special Issue
Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field
Previous Article in Journal
Convolutional Neural Networks for On-Board Cloud Screening
Previous Article in Special Issue
Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities
Article Menu
Issue 12 (June-2) cover image

Export Article

Open AccessArticle

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

1
School of Electronic Information, Wuhan University, Wuhan 430079, China
2
Department of Public Courses, Wuhan Railway Vocational College of Technology, Wuhan 430205, China
3
School of Mechanical & Electrical and Information Engineering, Hubei Business College, Wuhan 430000, China
4
Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430000, China
5
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; https://doi.org/10.3390/rs11121418
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]
  |  

Abstract

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
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top