| Accurate Registration of Remote Sensing Images Based on Local Optimal Transformation |
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| 作者姓名： | Bo Wang Changqing Li Shi Tang Zhiqiang Zhou Hong Zhao |
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| 作者单位： | School of Automation, Beijing Institute of Technology, Beijing 100081, China,School of Automation, Beijing Institute of Technology, Beijing 100081, China,Beijing Urban Construction Design & Development Group Co., Ltd., Beijing 100071, China,School of Automation, Beijing Institute of Technology, Beijing 100081, China,Beijing Navigation Control Technology Co., Ltd., Beijing 102200, China |
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| 摘 要： | As the basic work of image stitching and object recognition, image registration played an important part in the image processing field. Much previous work in registration accuracy and real-time performance progressed very slowly, especially in registrating images with line feature. An innovative method for image registration based on lines is proposed, it can effectively improve the accuracy and real-time performance of image registration. The line feature can deal with some registration problems where point feature does not work. Our registration process is divided into two parts. The first part determines the rough registration transformation relation between reference image and test image. Then the similarity degree among different transformation and modified non-maximum suppression (MNMS) algorithms are obtained, which produce local optimal solution to optimize the rough registration transformation. The final optimal registration relation can be obtained from two registration parts according to the match scores. The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.
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| 关 键 词： | initial registration relationship accurate registration relationship similarity degree local optimal transformation modified non-maximum suppression (MNMS) algorithm |
| 收稿时间： | 2017/12/15 0:00:00 |
Accurate Registration of Remote Sensing Images Based on Local Optimal Transformation |
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| Bo Wang,Changqing Li,Shi Tang,Zhiqiang Zhou,Hong Zhao.Accurate Registration of Remote Sensing Images Based on Local Optimal Transformation[J].Journal of Beijing Institute of Technology,2019,28(2):371-382. |
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| Authors: | Bo Wang Changqing Li Shi Tang Zhiqiang Zhou and Hong Zhao |
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| Affiliation: | School of Automation, Beijing Institute of Technology, Beijing 100081, China,School of Automation, Beijing Institute of Technology, Beijing 100081, China,Beijing Urban Construction Design & Development Group Co., Ltd., Beijing 100071, China,School of Automation, Beijing Institute of Technology, Beijing 100081, China and Beijing Navigation Control Technology Co., Ltd., Beijing 102200, China |
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| Abstract: | As the basic work of image stitching and object recognition, image registration played an important part in the image processing field. Much previous work in registration accuracy and real-time performance progressed very slowly, especially in registrating images with line feature. An innovative method for image registration based on lines is proposed, it can effectively improve the accuracy and real-time performance of image registration. The line feature can deal with some registration problems where point feature does not work. Our registration process is divided into two parts. The first part determines the rough registration transformation relation between reference image and test image. Then the similarity degree among different transformation and modified non-maximum suppression (MNMS) algorithms are obtained, which produce local optimal solution to optimize the rough registration transformation. The final optimal registration relation can be obtained from two registration parts according to the match scores. The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation. |
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| Keywords: | initial registration relationship accurate registration relationship similarity degree local optimal transformation modified non-maximum suppression (MNMS) algorithm |
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