SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration
How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the sca...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/14/3469 |
| _version_ | 1827731917332545536 |
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| author | Xiangzeng Liu Xueling Xu Xiaodong Zhang Qiguang Miao Lei Wang Liang Chang Ruyi Liu |
| author_facet | Xiangzeng Liu Xueling Xu Xiaodong Zhang Qiguang Miao Lei Wang Liang Chang Ruyi Liu |
| author_sort | Xiangzeng Liu |
| collection | DOAJ |
| description | How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods. |
| first_indexed | 2024-03-11T00:42:26Z |
| format | Article |
| id | doaj.art-dbe883aa436d4e1585f4fbdeb0da5e26 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:42:26Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dbe883aa436d4e1585f4fbdeb0da5e262023-11-18T21:11:13ZengMDPI AGRemote Sensing2072-42922023-07-011514346910.3390/rs15143469SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image RegistrationXiangzeng Liu0Xueling Xu1Xiaodong Zhang2Qiguang Miao3Lei Wang4Liang Chang5Ruyi Liu6School of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaNavInfo Co., Ltd., Beijing 100028, ChinaGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaHow to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.https://www.mdpi.com/2072-4292/15/14/3469multimodal imagesimage registrationremote sensinggeometric deformationtransform prediction |
| spellingShingle | Xiangzeng Liu Xueling Xu Xiaodong Zhang Qiguang Miao Lei Wang Liang Chang Ruyi Liu SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration Remote Sensing multimodal images image registration remote sensing geometric deformation transform prediction |
| title | SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration |
| title_full | SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration |
| title_fullStr | SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration |
| title_full_unstemmed | SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration |
| title_short | SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration |
| title_sort | srtpn scale and rotation transform prediction net for multimodal remote sensing image registration |
| topic | multimodal images image registration remote sensing geometric deformation transform prediction |
| url | https://www.mdpi.com/2072-4292/15/14/3469 |
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