Open AccessCO-REGISTRATION AIRBORNE LIDAR POINT CLOUD DATA AND SYNCHRONOUS DIGITAL IMAGE REGISTRATION BASED ON COMBINED ADJUSTMENT
Author(s) -
Z. H. Yang,
Y. S. Zhang,
T. Zheng,
W. B. Lai,
Zheng Zou,
BaoWen Zou
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b1-259-2016
Subject(s) - computer vision , point cloud , pixel , artificial intelligence , computer science , image registration , orientation (vector space) , scale invariant feature transform , digital elevation model , matching (statistics) , terrain , digital image , point (geometry) , lidar , remote sensing , image (mathematics) , image processing , mathematics , geography , statistics , geometry , cartography
Aim at the problem of co-registration airborne laser point cloud data with the synchronous digital image, this paper proposed a registration method based on combined adjustment. By integrating tie point, point cloud data with elevation constraint pseudo observations, using the principle of least-squares adjustment to solve the corrections of exterior orientation elements of each image, high-precision registration results can be obtained. In order to ensure the reliability of the tie point, and the effectiveness of pseudo observations, this paper proposed a point cloud data constrain SIFT matching and optimizing method, can ensure that the tie points are located on flat terrain area. Experiments with the airborne laser point cloud data and its synchronous digital image, there are about 43 pixels error in image space using the original POS data. If only considering the bore-sight of POS system, there are still 1.3 pixels error in image space. The proposed method regards the corrections of the exterior orientation elements of each image as unknowns and the errors are reduced to 0.15 pixels.