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Open AccessArticle

A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization

by 1,2, 1,2,*, 1,2 and 3
1
School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
2
The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
3
College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(1), 47; https://doi.org/10.3390/rs13010047
Received: 8 December 2020 / Revised: 21 December 2020 / Accepted: 22 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and [email protected] (K ∈ {1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM’s accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record). View Full-Text
Keywords: cross-view image matching; geo-localization; UAV image localization; UAV navigation cross-view image matching; geo-localization; UAV image localization; UAV navigation
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MDPI and ACS Style

Ding, L.; Zhou, J.; Meng, L.; Long, Z. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization. Remote Sens. 2021, 13, 47. https://doi.org/10.3390/rs13010047

AMA Style

Ding L, Zhou J, Meng L, Long Z. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization. Remote Sensing. 2021; 13(1):47. https://doi.org/10.3390/rs13010047

Chicago/Turabian Style

Ding, Lirong; Zhou, Ji; Meng, Lingxuan; Long, Zhiyong. 2021. "A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization" Remote Sens. 13, no. 1: 47. https://doi.org/10.3390/rs13010047

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