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Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion

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Abstract

Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.

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Acknowledgments

This work was supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052), Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172), the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015), the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633), the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Jinggangshan University, Ji′an, 343009, China

    Huan Liu, Yun-Lan Tan & Chun-Juan Ouyang

  2. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, National Administration of Surveying, Mapping and Geoinformation (NASG), Ji′an, 343009, China

    Huan Liu, Yun-Lan Tan & Chun-Juan Ouyang

  3. College of Mechanical and Electronic, Jinggangshan University, Ji′an, 343009, China

    Gen-Fu Xiao

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  1. Huan Liu
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Corresponding author

Correspondence to Gen-Fu Xiao.

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Recommended by Associate Editor Jangmyung Lee

Huan Liu received the B. Sc. degree in computing science and technology from Nanjing Institute of Technology, China in 2004, the M. Sc. degree in software engineering from Jiangxi Normal University, China in 2008, and the Ph. D. degree in pattern recognition and intelligent system from Donghua University, China in 2014. She is currently an associate professor at College of Electric and Information Engineering, Jinggangshan University, China. Her research interests include machine vision, image processing and intelligent algorithm.

Gen-Fu Xiao received the B. Sc. degree in automation from Nanchang University, China in 2002, the M. Sc. degree in automation from Nanchang University, China in 2005, and the Ph. D. degree in mechatronic engineering from Nanchang University, China in 2014. He is currently a lecturer in School of Mechanical and Electrical Engineering, Jinggangshan University, China. His research interests include modeling and optimization.

Yun-Lan Tan received the B. Sc. degree in computer application technology from Jiangxi Normal University, China in 1996, the M. Sc. degree in computer application technology from East China Normal University, China in 2004, and the Ph. D. degree in computer science from Tongji University, China in 2016. Now she is an associate professor in School of Electrical and Information Engineering, Jinggangshan University, China. Her research interests include image processing and machine learning.

Chun-Juan Ouyang received the B. Sc. degree in computer science from Nanchang University, China in 2000, the M. Sc. degree in computer science from Huanan Normal University, China in 2005, and the Ph. D. degree in signal and information processing from Shenzhen University, China in 2012. She is currently an associate professor at College of Electric and Information Engineering, Jinggangshan University, China. Her research interests include steganography and steganalysis, intelligence optimization.

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Liu, H., Xiao, GF., Tan, YL. et al. Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion. Int. J. Autom. Comput. 16, 575–588 (2019). https://doi.org/10.1007/s11633-018-1163-6

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  • DOI: https://doi.org/10.1007/s11633-018-1163-6

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