Fast Automatic Registration of UAV Images via Bidirectional Matching
Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visibl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8566 |
| _version_ | 1797572249927024640 |
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| author | Xin Luo Zuqi Wei Yuwei Jin Xiao Wang Peng Lin Xufeng Wei Wenjian Zhou |
| author_facet | Xin Luo Zuqi Wei Yuwei Jin Xiao Wang Peng Lin Xufeng Wei Wenjian Zhou |
| author_sort | Xin Luo |
| collection | DOAJ |
| description | Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization. |
| first_indexed | 2024-03-10T20:54:36Z |
| format | Article |
| id | doaj.art-4fd447096ba844c39f101e65aac8f43a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:36Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4fd447096ba844c39f101e65aac8f43a2023-11-19T18:04:51ZengMDPI AGSensors1424-82202023-10-012320856610.3390/s23208566Fast Automatic Registration of UAV Images via Bidirectional MatchingXin Luo0Zuqi Wei1Yuwei Jin2Xiao Wang3Peng Lin4Xufeng Wei5Wenjian Zhou6Yangtze Delta Region Institute (HuZhou), University of Electronic Science and Technology of China, Huzhou 313099, ChinaYangtze Delta Region Institute (HuZhou), University of Electronic Science and Technology of China, Huzhou 313099, ChinaSchool of Electrical and Information Engineering, Panzhihua University, Panzhihua 617000, ChinaYangtze Delta Region Institute (HuZhou), University of Electronic Science and Technology of China, Huzhou 313099, ChinaYangtze Delta Region Institute (HuZhou), University of Electronic Science and Technology of China, Huzhou 313099, ChinaYangtze Delta Region Institute (HuZhou), University of Electronic Science and Technology of China, Huzhou 313099, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaImage registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.https://www.mdpi.com/1424-8220/23/20/8566UAVimage registrationORBpoint featurebidirectional matching |
| spellingShingle | Xin Luo Zuqi Wei Yuwei Jin Xiao Wang Peng Lin Xufeng Wei Wenjian Zhou Fast Automatic Registration of UAV Images via Bidirectional Matching Sensors UAV image registration ORB point feature bidirectional matching |
| title | Fast Automatic Registration of UAV Images via Bidirectional Matching |
| title_full | Fast Automatic Registration of UAV Images via Bidirectional Matching |
| title_fullStr | Fast Automatic Registration of UAV Images via Bidirectional Matching |
| title_full_unstemmed | Fast Automatic Registration of UAV Images via Bidirectional Matching |
| title_short | Fast Automatic Registration of UAV Images via Bidirectional Matching |
| title_sort | fast automatic registration of uav images via bidirectional matching |
| topic | UAV image registration ORB point feature bidirectional matching |
| url | https://www.mdpi.com/1424-8220/23/20/8566 |
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