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Remote Sens. 2016, 8(3), 213; doi:10.3390/rs8030213

Absolute Orientation Based on Distance Kernel Functions

1
Center for Remote Sensing, School of Architecture, Tianjin University, Tianjin 300072, China
2
Hamlyn Centre for Robotic Surgery, Department of Computing, Faculty of Engineering, Imperial College London, London SW7 2BX, UK
3
Guangxi Key Laboratory for Geomatics and Geoinformatics, Guilin University of Technology, Guilin 532100, China
4
Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad Thenkabail
Received: 25 November 2015 / Revised: 21 February 2016 / Accepted: 29 February 2016 / Published: 5 March 2016
View Full-Text   |   Download PDF [3543 KB, uploaded 5 March 2016]   |  

Abstract

The classical absolute orientation method is capable of transforming tie points (TPs) from a local coordinate system to a global (geodetic) coordinate system. The method is based only on a unique set of similarity transformation parameters estimated by minimizing the total difference between all ground control points (GCPs) and the fitted points. Nevertheless, it often yields a transformation with poor accuracy, especially in large-scale study cases. To address this problem, this study proposes a novel absolute orientation method based on distance kernel functions, in which various sets of similarity transformation parameters instead of only one set are calculated. When estimating the similarity transformation parameters for TPs using the iterative solution of a non-linear least squares problem, we assigned larger weighting matrices for the GCPs for which the distances from the point are short. The weighting matrices can be evaluated using the distance kernel function as a function of the distances between the GCPs and the TPs. Furthermore, we used the exponential function and the Gaussian function to describe distance kernel functions in this study. To validate and verify the proposed method, six synthetic and two real datasets were tested. The accuracy was significantly improved by the proposed method when compared to the classical method, although a higher computational complexity is experienced. View Full-Text
Keywords: absolute orientation; weighting matrix; kernel function; least squares problem absolute orientation; weighting matrix; kernel function; least squares problem
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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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Sun, Y.; Zhao, L.; Zhou, G.; Yan, L. Absolute Orientation Based on Distance Kernel Functions. Remote Sens. 2016, 8, 213.

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