Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer
Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switchi...
| मुख्य लेखकों: | , , , , , , , |
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| स्वरूप: | लेख |
| भाषा: | English |
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MDPI AG
2024-07-01
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| श्रृंखला: | Drones |
| विषय: | |
| ऑनलाइन पहुंच: | https://www.mdpi.com/2504-446X/8/7/329 |
| _version_ | 1827178385867014144 |
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| author | Hong Liu Bingliang Hu Xingsong Hou Tao Yu Zhoufeng Zhang Xiao Liu Jiacheng Liu Xueji Wang |
| author_facet | Hong Liu Bingliang Hu Xingsong Hou Tao Yu Zhoufeng Zhang Xiao Liu Jiacheng Liu Xueji Wang |
| author_sort | Hong Liu |
| collection | DOAJ |
| description | Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles. |
| first_indexed | 2025-03-21T04:58:18Z |
| format | Article |
| id | doaj.art-bf1d78fb65be4e8aaa46e16f7d45a9e6 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2025-03-21T04:58:18Z |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-bf1d78fb65be4e8aaa46e16f7d45a9e62024-07-26T12:41:14ZengMDPI AGDrones2504-446X2024-07-018732910.3390/drones8070329Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter SpectrometerHong Liu0Bingliang Hu1Xingsong Hou2Tao Yu3Zhoufeng Zhang4Xiao Liu5Jiacheng Liu6Xueji Wang7Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaSchool of Electronic and Information Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaDifferences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.https://www.mdpi.com/2504-446X/8/7/329acousto-optic tunable filterimage registrationreal-time processingspectral imagingUAV remote sensing |
| spellingShingle | Hong Liu Bingliang Hu Xingsong Hou Tao Yu Zhoufeng Zhang Xiao Liu Jiacheng Liu Xueji Wang Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer Drones acousto-optic tunable filter image registration real-time processing spectral imaging UAV remote sensing |
| title | Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer |
| title_full | Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer |
| title_fullStr | Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer |
| title_full_unstemmed | Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer |
| title_short | Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer |
| title_sort | real time registration of unmanned aerial vehicle hyperspectral remote sensing images using an acousto optic tunable filter spectrometer |
| topic | acousto-optic tunable filter image registration real-time processing spectral imaging UAV remote sensing |
| url | https://www.mdpi.com/2504-446X/8/7/329 |
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