A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints
In this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9428514/ |
| _version_ | 1798032811367596032 |
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| author | Yue Wu Zhenglei Xiao Shaodi Liu Qiguang Miao Wenping Ma Maoguo Gong Fei Xie Yang Zhang |
| author_facet | Yue Wu Zhenglei Xiao Shaodi Liu Qiguang Miao Wenping Ma Maoguo Gong Fei Xie Yang Zhang |
| author_sort | Yue Wu |
| collection | DOAJ |
| description | In this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods. |
| first_indexed | 2024-04-11T20:18:49Z |
| format | Article |
| id | doaj.art-1591d9e91e1a47c6bd45d43d1fe96d14 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T20:18:49Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-1591d9e91e1a47c6bd45d43d1fe96d142022-12-22T04:04:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01145194520610.1109/JSTARS.2021.30791039428514A Two-Step Method for Remote Sensing Images Registration Based on Local and Global ConstraintsYue Wu0https://orcid.org/0000-0002-3459-5079Zhenglei Xiao1Shaodi Liu2Qiguang Miao3https://orcid.org/0000-0001-6766-8310Wenping Ma4https://orcid.org/0000-0001-8872-2195Maoguo Gong5https://orcid.org/0000-0002-0415-8556Fei Xie6Yang Zhang7School of Computer Science and Technology, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, School of Artficial Intelligence, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, School of Artficial Intelligence, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, ChinaAcademy of Advanced Interdisciplinary Research, Xidian University, Xi’an, ChinaShanghai Aerospace Electronic Technology Institute, Shanghai, ChinaIn this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.https://ieeexplore.ieee.org/document/9428514/Feature descriptorglobal informationimage registrationlocality preservingscale-invariant feature transform (SIFT) |
| spellingShingle | Yue Wu Zhenglei Xiao Shaodi Liu Qiguang Miao Wenping Ma Maoguo Gong Fei Xie Yang Zhang A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Feature descriptor global information image registration locality preserving scale-invariant feature transform (SIFT) |
| title | A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints |
| title_full | A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints |
| title_fullStr | A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints |
| title_full_unstemmed | A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints |
| title_short | A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints |
| title_sort | two step method for remote sensing images registration based on local and global constraints |
| topic | Feature descriptor global information image registration locality preserving scale-invariant feature transform (SIFT) |
| url | https://ieeexplore.ieee.org/document/9428514/ |
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