Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS
Unmanned aerial vehicle (UAV) remote sensing has found extensive applications in various fields due to its ability to quickly provide remote sensing imagery, and the rapid, even automated, geometric registration of these images is an important component of their time efficiency. While current geomet...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/723 |
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| author | Pengfei Li Yu Zhang Yepei Chen Ting Bai Kaimin Sun Haigang Sui Yang Wu |
| author_facet | Pengfei Li Yu Zhang Yepei Chen Ting Bai Kaimin Sun Haigang Sui Yang Wu |
| author_sort | Pengfei Li |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) remote sensing has found extensive applications in various fields due to its ability to quickly provide remote sensing imagery, and the rapid, even automated, geometric registration of these images is an important component of their time efficiency. While current geometric registration methods based on image matching are well developed, there is still room for improvement in terms of time efficiency due to the presence of the following factors: (1) difficulty in accessing historical reference images and (2) inconsistencies in data sources, scales, and orientations between UAV imagery and reference images, which leads to unreliable matching. To further improve the time efficiency of UAV remote sensing, this study proposes a fully automatic geometric registration framework. The workflow features the following aspects: (1) automatic reference image acquisition by using online map services; (2) automatic ground range and resolution estimation using positional and orientation system (POS) data; (3) automatic orientation alignment using POS data. Experimental validation demonstrates that the proposed framework is able to carry out the fully automatic geometric registration of UAV imagery, thus improving the time efficiency of UAV remote sensing. |
| format | Article |
| id | doaj-art-e1b95eec88254a46baaa08a18034c007 |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e1b95eec88254a46baaa08a18034c0072025-08-20T02:55:36ZengMDPI AGDrones2504-446X2024-11-0181272310.3390/drones8120723Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POSPengfei Li0Yu Zhang1Yepei Chen2Ting Bai3Kaimin Sun4Haigang Sui5Yang Wu6School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, ChinaCollege of Information and Communication, National University of Defense Technology, Wuhan 430010, ChinaUnmanned aerial vehicle (UAV) remote sensing has found extensive applications in various fields due to its ability to quickly provide remote sensing imagery, and the rapid, even automated, geometric registration of these images is an important component of their time efficiency. While current geometric registration methods based on image matching are well developed, there is still room for improvement in terms of time efficiency due to the presence of the following factors: (1) difficulty in accessing historical reference images and (2) inconsistencies in data sources, scales, and orientations between UAV imagery and reference images, which leads to unreliable matching. To further improve the time efficiency of UAV remote sensing, this study proposes a fully automatic geometric registration framework. The workflow features the following aspects: (1) automatic reference image acquisition by using online map services; (2) automatic ground range and resolution estimation using positional and orientation system (POS) data; (3) automatic orientation alignment using POS data. Experimental validation demonstrates that the proposed framework is able to carry out the fully automatic geometric registration of UAV imagery, thus improving the time efficiency of UAV remote sensing.https://www.mdpi.com/2504-446X/8/12/723unmanned aerial vehicle (UAV)geometric registrationonline map servicesfeature matching |
| spellingShingle | Pengfei Li Yu Zhang Yepei Chen Ting Bai Kaimin Sun Haigang Sui Yang Wu Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS Drones unmanned aerial vehicle (UAV) geometric registration online map services feature matching |
| title | Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS |
| title_full | Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS |
| title_fullStr | Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS |
| title_full_unstemmed | Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS |
| title_short | Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS |
| title_sort | fully automatic geometric registration framework of uav imagery based on online map services and pos |
| topic | unmanned aerial vehicle (UAV) geometric registration online map services feature matching |
| url | https://www.mdpi.com/2504-446X/8/12/723 |
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