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Open AccessArticle

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Remote Sensing Technology Institute, German Aerospace Center, 82234 Wessling, Germany
Remote Sensing Technology, Technische Universität München, 80333 Munich, Germany
Institute for Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 376;
Received: 14 February 2017 / Revised: 14 February 2017 / Accepted: 9 April 2017 / Published: 17 April 2017
Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images. View Full-Text
Keywords: unmanned aerial vehicle; image registration; geo-registration; point cloud unmanned aerial vehicle; image registration; geo-registration; point cloud
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MDPI and ACS Style

Zhuo, X.; Koch, T.; Kurz, F.; Fraundorfer, F.; Reinartz, P. Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data. Remote Sens. 2017, 9, 376.

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