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Open Access

Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction

Xijia LiuXiaoming Tao( )Ning Ge
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
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In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images, and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art Scale-Invariant Feature Transform (SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7.47 while maintaining the accuracy of the image registration performance.


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Tsinghua Science and Technology
Pages 552-560
Cite this article:
Liu X, Tao X, Ge N. Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction. Tsinghua Science and Technology, 2016, 21(5): 552-560.








Web of Science






Received: 27 August 2015
Accepted: 31 December 2015
Published: 18 October 2016
© The author(s) 2016