Next Article in Journal
Fast Detection of Oil Spills and Ships Using SAR Images
Next Article in Special Issue
Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data
Previous Article in Journal
Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
Previous Article in Special Issue
Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 244;

A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration

School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
School of Marine Sciences, Guangxi University, 100 Daxue East Road, Nanning 530004, China
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Jixian Zhang, Xiangguo Lin and Prasad S. Thenkabail
Received: 19 January 2017 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 6 March 2017
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
Full-Text   |   PDF [4470 KB, uploaded 6 March 2017]   |  


Compared with prime lenses, zoom lenses have inherent advantages in terms of operational flexibility. Zoom lens camera systems have therefore been extensively adopted in computer vision where precise measurement is not the primary objective. However, the variation of intrinsic camera parameters with respect to zoom lens settings poses a series of calibration challenges that have inhibited widespread use in close-range photogrammetry. A flexible zoom lens calibration methodology is therefore proposed in this study, developed with the aim of simplifying the calibration process and promoting practical photogrammetric application. A zoom-dependent camera model that incorporates empirical zoom-related intrinsic parameters into the collinearity condition equations is developed. Coefficients of intrinsic parameters are solved in a single adjustment based on this zoom lens camera model. To validate the approach, experiments on both optical- and digital-zoom lens cameras were conducted using a planar board with evenly distributed circular targets. Zoom lens calibration was performed with images taken at four different zoom settings spread throughout the zoom range of a lens. Photogrammetric accuracies achieved through both mono-focal and multi-focal triangulations were evaluated after calibration. The relative accuracies for mono-focal triangulations ranged from 1: 6300 to 1: 18,400 for the two cameras studied, whereas the multi-focal triangulation accuracies ranged from 1: 11,300 to 1: 16,200. In order to demonstrate the applicability of the approach, calibrated zoom lens imagery was used to render a laser-scanned point cloud of a building façade. Considered alongside experimental results, the successful application demonstrates the feasibility of the proposed calibration method, thereby facilitating the adoption of zoom lens cameras in close range photogrammetry for a wide range of scientific and practical applications. View Full-Text
Keywords: calibration; digital camera; point cloud; texture; triangulation; zoom lens calibration; digital camera; point cloud; texture; triangulation; zoom lens

Graphical abstract

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

Wang, Z.; Mills, J.; Xiao, W.; Huang, R.; Zheng, S.; Li, Z. A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration. Remote Sens. 2017, 9, 244.

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