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J. Imaging 2017, 3(3), 32; https://doi.org/10.3390/jimaging3030032

Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration

Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
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Received: 8 May 2017 / Revised: 19 July 2017 / Accepted: 20 July 2017 / Published: 22 July 2017
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Abstract

The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution. View Full-Text
Keywords: image registration; image rectification; remote sensing; ground control lines; line-based transformation model image registration; image rectification; remote sensing; ground control lines; line-based transformation model
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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).
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Shaker, A.; Easa, S.M.; Yan, W.Y. Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration. J. Imaging 2017, 3, 32.

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