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

Abstract

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.

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

“…Image matching is one of the fundamental technologies in photogrammetry and computer vision, which is widely used in image registration, image stitching, 3D reconstruction, etc. [67][68][69]. It is a long-standing and challenging task, especially for UAV images, due to the strong geometric deformations (e.g.…”

“…In fact, limited progresses have been made in deep feature detection, due to the lack of large-scale annotated data and the difficulty to get a clear definition about keypoints. By contrast, great efforts have been made on developing learned descriptors based on CNNs, which have obtained (a) Matching nadir and oblique images [70] (b) Matching ground to aerial images [71] (c) Matching UAV image to geo-reference images [68] Figure 10: Low-altitude UAV image matching.…”

Hierarchical motion consistency constraint for efficient geometrical verification in UAV stereo image matching

“…Considering different characteristics of images captured in the field of photogrammetry, some considerations for geometrical verification have been documented. For UAV image georegistration, Zhuo et al (2017) proposed a matching pipeline with pixel-distances as a global geometrical constraint, where a histogram voting technique for location changes of matches was used to verify initial matches after the elimination of differences in the rotation and scale. Tsai and Lin (2017) suggested checking whether the distances of feature points in horizontal and vertical directions are similar to others, because the UAV orthoimages have been coarsely aligned.…”

Automatic Annotation of Airborne Images by Label Propagation Based on a Bayesian-CRF Model

“…A high co-registration accuracy is vital to the label propagation between multi-source image data. As the UAV images in our dataset exhibit a much lower geolocalization accuracy than aerial images, we adopted the approach proposed in [25] for co-registration between UAV and aerial images. In short, the method assumes that the aerial images are geo-referenced and have common overlap with UAV images.…”