Document Type
Article
Publication Date
2016
DOI
10.3390/rs8060507
Publication Title
Remote Sensing
Volume
8
Issue
7
Pages
1-17
Abstract
This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings.
Original Publication Citation
Zhou, G.Q., Luo, Q.L., Xie, W.H., Yue, T., Huang, J.J., & Shen, Y.Z. (2016). Transformation model with constraints for high-accuracy of 2D-3D building registration in aerial imagery. Remote Sensing, 8(6), 17. doi: 10.3390/rs8060507
Repository Citation
Zhou, Guoqing; Luo, Qingli; Xie, Wenhan; Yue, Tao; Huang, Jingjin; and Shen, Yuzhong, "Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery" (2016). Computational Modeling & Simulation Engineering Faculty Publications. 3.
https://digitalcommons.odu.edu/msve_fac_pubs/3
Comments
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/)