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.
Highlights
We propose a new strategy for matching aerial images and UAV images, and compare the performance of the proposed method with conventional SIFT and A-SIFT approaches on different datasets
This paper dealt with SIFT-based image matching on problematic image pairs, like low altitude UAV images and high altitude aerial images
That the state-of-the-art SIFT and A-SIFTmethods often fail in case of large differences in image scaling, rotation and temporal changes of the scene
Summary
Very good results can be achieved, even under very difficult conditions, it is (a) interesting that there are still examples, where image matching is very problematic or even fails, surprisingly even for cases of image pairs that look very similar. In our context, this problem can be seen when trying to match UAV images and aerial images, which differ in geometric and temporal changes.
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