Document Type
Article
Publication Date
8-2015
Publication Source
ISPRS Journal of Photogrammetry and Remote Sensing
Abstract
This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds.
The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable.
Inclusive pages
68–81
ISBN/ISSN
0924-2716
Document Version
Postprint
Copyright
Copyright © 2015, Elsevier
Publisher
Elsevier
Volume
106
Peer Reviewed
yes
eCommons Citation
Abayowa, Bernard Olushola; Yilmaz, Alper; and Hardie, Russell C., "Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of City Models" (2015). Electrical and Computer Engineering Faculty Publications. 363.
https://ecommons.udayton.edu/ece_fac_pub/363
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
The author's accepted manuscript is provided for download in compliance with the publisher's policy on self-archiving. There may be some differences between this version and the published version. To read the published version, use the DOI provided.
Permission documentation is on file.