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Open AccessArticle

A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery

by Tengfei Long 1,2, Weili Jiao 1,2,*, Guojin He 1,2 and Zhaoming Zhang 1,2
1
The Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
2
Hainan Key Laboratory for Earth Observation, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Academic Editors: Josef Kellndorfer, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Remote Sens. 2016, 8(1), 56; https://doi.org/10.3390/rs8010056
Received: 10 December 2015 / Revised: 2 January 2016 / Accepted: 4 January 2016 / Published: 19 January 2016
Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images. View Full-Text
Keywords: image matching; SIFT; sub-pixel precision; rectification; well-distributed; matching online image matching; SIFT; sub-pixel precision; rectification; well-distributed; matching online
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Long, T.; Jiao, W.; He, G.; Zhang, Z. A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery. Remote Sens. 2016, 8, 56.

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