10 April 2018 Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061549 (2018) https://doi.org/10.1117/12.2304527
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
In the process of geometric correction of remote sensing image, occasionally, a large number of redundant control points may result in low correction accuracy. In order to solve this problem, a control points filtering algorithm based on RANdom SAmple Consensus (RANSAC) was proposed. The basic idea of the RANSAC algorithm is that using the smallest data set possible to estimate the model parameters and then enlarge this set with consistent data points. In this paper, unlike traditional methods of geometric correction using Ground Control Points (GCPs), the simulation experiments are carried out to correct remote sensing images, which using visible stars as control points. In addition, the accuracy of geometric correction without Star Control Points (SCPs) optimization is also shown. The experimental results show that the SCPs’s filtering method based on RANSAC algorithm has a great improvement on the accuracy of remote sensing image correction.
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Xiangli Tan, Jungang Yang, Xinpu Deng, "Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061549 (10 April 2018); doi: 10.1117/12.2304527; https://doi.org/10.1117/12.2304527
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