Refining multi-modal remote sensing image matching with repetitive feature optimization
Existing methods for matching multi-modal remote sensing images (MRSI) demonstrate considerable adaptability. However, high-precision matching for rectification remains challenging due to differing imaging mechanisms in cross-modal remote sensing images, leading to numerous non-repeated detailed fea...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224005429 |
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| author | Yifan Liao Ke Xi Huijin Fu Lai Wei Shuo Li Qiang Xiong Qi Chen Pengjie Tao Tao Ke |
| author_facet | Yifan Liao Ke Xi Huijin Fu Lai Wei Shuo Li Qiang Xiong Qi Chen Pengjie Tao Tao Ke |
| author_sort | Yifan Liao |
| collection | DOAJ |
| description | Existing methods for matching multi-modal remote sensing images (MRSI) demonstrate considerable adaptability. However, high-precision matching for rectification remains challenging due to differing imaging mechanisms in cross-modal remote sensing images, leading to numerous non-repeated detailed feature points. Additionally, assuming linear transformations between images conflicts with the complex aberrations present in remote sensing images, limiting matching accuracy. This paper aims to elevate matching accuracy by implementing a detailed texture removal strategy that effectively isolates repeatable structural features. Subsequently, we construct a radiation-invariant similarity function within a generalized gradient framework for least-squares matching, specifically designed to mitigate nonlinear geometric and radiometric distortions across MRSIs. Comprehensive qualitative and quantitative evaluations across multiple datasets, employing substantial manual checkpoints, demonstrate that our method significantly enhances matching accuracy for image data involving multiple modal combinations and outperforms the current state-of-the-art solutions in matching accuracy. Additionally, rectification experiments employing WorldView and TanDEM-X images validate our method’s ability to achieve a matching accuracy of 1.05 pixels, thereby indicating its practical utility and generalization capacity. Access to experiment-related data and codes will be provided at https://github.com/LiaoYF001/refinement/. |
| format | Article |
| id | doaj-art-965bab8cda9246d0aac93c7abd2b12d8 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-965bab8cda9246d0aac93c7abd2b12d82024-11-16T05:10:08ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-11-01134104186Refining multi-modal remote sensing image matching with repetitive feature optimizationYifan Liao0Ke Xi1Huijin Fu2Lai Wei3Shuo Li4Qiang Xiong5Qi Chen6Pengjie Tao7Tao Ke8School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaKey Laboratory of Smart Earth, Beijing 100094, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China; Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, 315200, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; Key Laboratory of Smart Earth, Beijing 100094, China; Hubei Luojia Laboratory, Wuhan 430079, China; Corresponding authors at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan 430079, China; Corresponding authors at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.Existing methods for matching multi-modal remote sensing images (MRSI) demonstrate considerable adaptability. However, high-precision matching for rectification remains challenging due to differing imaging mechanisms in cross-modal remote sensing images, leading to numerous non-repeated detailed feature points. Additionally, assuming linear transformations between images conflicts with the complex aberrations present in remote sensing images, limiting matching accuracy. This paper aims to elevate matching accuracy by implementing a detailed texture removal strategy that effectively isolates repeatable structural features. Subsequently, we construct a radiation-invariant similarity function within a generalized gradient framework for least-squares matching, specifically designed to mitigate nonlinear geometric and radiometric distortions across MRSIs. Comprehensive qualitative and quantitative evaluations across multiple datasets, employing substantial manual checkpoints, demonstrate that our method significantly enhances matching accuracy for image data involving multiple modal combinations and outperforms the current state-of-the-art solutions in matching accuracy. Additionally, rectification experiments employing WorldView and TanDEM-X images validate our method’s ability to achieve a matching accuracy of 1.05 pixels, thereby indicating its practical utility and generalization capacity. Access to experiment-related data and codes will be provided at https://github.com/LiaoYF001/refinement/.http://www.sciencedirect.com/science/article/pii/S1569843224005429Multi-modal remote sensing imagesMatching refinementRepetitive featureLeast-squares matchingNonlinear radiometric distortion |
| spellingShingle | Yifan Liao Ke Xi Huijin Fu Lai Wei Shuo Li Qiang Xiong Qi Chen Pengjie Tao Tao Ke Refining multi-modal remote sensing image matching with repetitive feature optimization International Journal of Applied Earth Observations and Geoinformation Multi-modal remote sensing images Matching refinement Repetitive feature Least-squares matching Nonlinear radiometric distortion |
| title | Refining multi-modal remote sensing image matching with repetitive feature optimization |
| title_full | Refining multi-modal remote sensing image matching with repetitive feature optimization |
| title_fullStr | Refining multi-modal remote sensing image matching with repetitive feature optimization |
| title_full_unstemmed | Refining multi-modal remote sensing image matching with repetitive feature optimization |
| title_short | Refining multi-modal remote sensing image matching with repetitive feature optimization |
| title_sort | refining multi modal remote sensing image matching with repetitive feature optimization |
| topic | Multi-modal remote sensing images Matching refinement Repetitive feature Least-squares matching Nonlinear radiometric distortion |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224005429 |
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