The Performance Evaluation of Image Matching Techniques within UAV Images

Year 2020, Volume: 1 Issue: 1, 8 - 14, 15.06.2020

Hasan Bilgehan Makineci , Hakan Karabörk , Akif Durdu

Abstract

The process of producing the orthomosaic image can be done in two different ways: image mosaicing and image stitching. In image stitching, there are small overlaps between images in which the images are put together. In contrast, image mosaic requires extended overlaps and a blending of several images.

In this study, used images grouped by inclination are also classified according to the type of UAV used. This classification is based on the wing type of the UAS. “Rotary-Wing UAV” and “Fixed-Wing UAV" are selected as two different types at the test field. Images with different characteristics were used to produce mosaics from the algorithms. The first performance preferred a flight height of 30 m (Ground Sample Distance, 0.82 cm/pixel) with the frontal overlap of 80%, and the second performance preferred a flight height of 60 m (GSD, 1.64 cm/pixel) and same overlap. 10 images from both studies were combined in all algorithms.
According to the results produced from the mosaics of these images, for rotary-wing UAV (SURF, BRISK and FAST), the algorithm run times were determined as 76.5 minutes, 11 minutes and 1839 minutes. Also, for fixed-wing UAV (SURF, BRISK and FAST), algorithm run times of 238 minutes, 95 minutes and 3350 minutes were determined.

In this study, it is aimed to mosaic outdoor images taken with two different types of UAS by feature-based transformation algorithms such as SURF, FAST and BRISK. In case, mismatches have been observed and the mosaics produced after a very long process are unsatisfactory.

Keywords

BRISK, FAST, Image Mosaicing, SURF, Unmanned Aerial System (UAS)

References

• Aslan, Muhammet Fatih, Akif Durdu, and Kadir Sabanci. "Fusion of CT and MR Liver Images by SURF-Based Registration." International Journal of Intelligent Systems and Applications in Engineering 7.4 (2019): 216-221.
• Cen, Chen, Runqi Li, and Xi Xu. "Fast robust matching algorithm based on BRISK and GMS." Journal of Physics: Conference Series. Vol. 1237. No. 2. IOP Publishing, 2019.
• Durdu, Akif, and Mehmet Korkmaz. "Autonomously simultaneous localization and mapping based on line tracking in a factory-like environment." Advances in Electrical and Electronic Engineering 17.1 (2019): 45-53.
• Greengard, Leslie, and Vladimir Rokhlin. A fast algorithm for particle simulations. No. Yaleu/dcs/rr-459. Yale univ new haven ct dept of computer science, 1986.
• Juan, Luo, and Gwun Oubong. "SURF applied in panorama image stitching." 2010 2nd international conference on image processing theory, tools and applications. IEEE, 2010.
• Karasaka, Lütfiye, et al. "Geometric analysis of digital aerial images." International Journal of Environment and Geoinformatics 4.1 (2017): 36-42.
• Makineci, Hasan Bilgehan. "İnsansız Hava Araçları Lidar Etkileşimi." Geomatik 1.1 (2016): 19-23.
• Makineci, H. B., and H. Karabörk. "Evaluation digital elevation model generated by synthetic aperture radar data." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 (2016).
• Rublee, Ethan, et al. "ORB: An efficient alternative to SIFT or SURF." 2011 International conference on computer vision. Ieee, 2011.
• Zhong, Bing, and Yubai Li. "Image Feature Point Matching Based on Improved SIFT Algorithm." 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2019.