Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/rs8030213 | REMOTE SENSING |
Keywords | Field | DocType |
weighting matrix,absolute orientation,kernel function,least squares problem | Coordinate system,Least squares,Weighting,Radial basis function kernel,Matrix (mathematics),Artificial intelligence,Gaussian function,Computer vision,Mathematical optimization,Exponential function,Algorithm,Mathematics,Kernel (statistics) | Journal |
Volume | Issue | Citations |
8 | 3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanbiao Sun | 1 | 5 | 1.11 |
| Liang Zhao | 2 | 100 | 13.74 |
| Guoqing Zhou | 3 | 25 | 15.98 |
| Lei Yan | 4 | 76 | 31.94 |