Volume III-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 83-90, 2016
https://doi.org/10.5194/isprs-annals-III-3-83-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 83-90, 2016
https://doi.org/10.5194/isprs-annals-III-3-83-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Jun 2016

03 Jun 2016

A NEW PARADIGM FOR MATCHING UAV- AND AERIAL IMAGES

T. Koch1, X. Zhuo2, P. Reinartz2, and F. Fraundorfer2,3 T. Koch et al.
  • 1Remote Sensing Technology, Technische Universität München, 80333 München, Germany
  • 2The Remote Sensing Technology Institute, German Aerospace Center, 82234 Wessling, Germany
  • 3Institute for Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria

Keywords: Image matching, Feature-based matching, Image registration, Geo-registration, 3D Reconstruction, Navigation, SIFT, A-SIFT

Abstract. This paper investigates the performance of SIFT-based image matching regarding large differences in image scaling and rotation, as this is usually the case when trying to match images captured from UAVs and airplanes. This task represents an essential step for image registration and 3d-reconstruction applications. Various real world examples presented in this paper show that SIFT, as well as A-SIFT perform poorly or even fail in this matching scenario. Even if the scale difference in the images is known and eliminated beforehand, the matching performance suffers from too few feature point detections, ambiguous feature point orientations and rejection of many correct matches when applying the ratio-test afterwards. Therefore, a new feature matching method is provided that overcomes these problems and offers thousands of matches by a novel feature point detection strategy, applying a one-to-many matching scheme and substitute the ratio-test by adding geometric constraints to achieve geometric correct matches at repetitive image regions. This method is designed for matching almost nadir-directed images with low scene depth, as this is typical in UAV and aerial image matching scenarios. We tested the proposed method on different real world image pairs. While standard SIFT failed for most of the datasets, plenty of geometrical correct matches could be found using our approach. Comparing the estimated fundamental matrices and homographies with ground-truth solutions, mean errors of few pixels can be achieved.