Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network
Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, ra...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/838 |
| _version_ | 1797476819695304704 |
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| author | Chuan Xu Chang Liu Hongli Li Zhiwei Ye Haigang Sui Wei Yang |
| author_facet | Chuan Xu Chang Liu Hongli Li Zhiwei Ye Haigang Sui Wei Yang |
| author_sort | Chuan Xu |
| collection | DOAJ |
| description | Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions. |
| first_indexed | 2024-03-09T21:09:09Z |
| format | Article |
| id | doaj.art-5b947b8fa214450fa8e8e7c166763e2f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:09:09Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5b947b8fa214450fa8e8e7c166763e2f2023-11-23T21:52:53ZengMDPI AGRemote Sensing2072-42922022-02-0114483810.3390/rs14040838Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural NetworkChuan Xu0Chang Liu1Hongli Li2Zhiwei Ye3Haigang Sui4Wei Yang5School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430070, 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 430072, ChinaSchool of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, ChinaMatching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions.https://www.mdpi.com/2072-4292/14/4/838multiviewsatellite and UAV imagejoint descriptionimage matchingneural network |
| spellingShingle | Chuan Xu Chang Liu Hongli Li Zhiwei Ye Haigang Sui Wei Yang Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network Remote Sensing multiview satellite and UAV image joint description image matching neural network |
| title | Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network |
| title_full | Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network |
| title_fullStr | Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network |
| title_full_unstemmed | Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network |
| title_short | Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network |
| title_sort | multiview image matching of optical satellite and uav based on a joint description neural network |
| topic | multiview satellite and UAV image joint description image matching neural network |
| url | https://www.mdpi.com/2072-4292/14/4/838 |
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