Next Article in Journal
Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago
Previous Article in Journal
Characterization of Available Light for Seagrass and Patch Reef Productivity in Sugarloaf Key, Lower Florida Keys
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(2), 82;

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China
Jiangsu Power Design Institute Co., Ltd. of China Energy Engineering Group, 58-3 Suyuan Avenue, Nanjing 211102, China
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle and Prasad S. Thenkabail
Received: 6 October 2015 / Revised: 9 December 2015 / Accepted: 11 January 2016 / Published: 25 January 2016
Full-Text   |   PDF [2632 KB, uploaded 25 January 2016]   |  


Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Förstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved. View Full-Text
Keywords: unmanned aerial vehicle; optical images; LiDAR; linear feature; registration unmanned aerial vehicle; optical images; LiDAR; linear feature; registration

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Liu, S.; Tong, X.; Chen, J.; Liu, X.; Sun, W.; Xie, H.; Chen, P.; Jin, Y.; Ye, Z. A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data. Remote Sens. 2016, 8, 82.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top