Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images
Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-sca...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7721 |
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| author | Wei Wang Jinyang Chen Zhonghua Hong |
| author_facet | Wei Wang Jinyang Chen Zhonghua Hong |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU–SAR–SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR–Shi–Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR–SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU–SAR–SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved. |
| format | Article |
| id | doaj-art-d2e420e60db34afaab083546e43ec68b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-d2e420e60db34afaab083546e43ec68b2025-08-20T03:35:27ZengMDPI AGApplied Sciences2076-34172025-07-011514772110.3390/app15147721Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR ImagesWei Wang0Jinyang Chen1Zhonghua Hong2College of Surveying and GEO-Informatics, Tongji University, No. 1239 Si Ping Road, Shanghai 200092, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaImage matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU–SAR–SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR–Shi–Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR–SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU–SAR–SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved.https://www.mdpi.com/2076-3417/15/14/7721synthetic aperture radar imagerySAR–SIFTSUSANfork/joinfeature-based methods |
| spellingShingle | Wei Wang Jinyang Chen Zhonghua Hong Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images Applied Sciences synthetic aperture radar imagery SAR–SIFT SUSAN fork/join feature-based methods |
| title | Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images |
| title_full | Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images |
| title_fullStr | Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images |
| title_full_unstemmed | Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images |
| title_short | Multiscale Eight Direction Descriptor-Based Improved SAR–SIFT Method for Along-Track and Cross-Track SAR Images |
| title_sort | multiscale eight direction descriptor based improved sar sift method for along track and cross track sar images |
| topic | synthetic aperture radar imagery SAR–SIFT SUSAN fork/join feature-based methods |
| url | https://www.mdpi.com/2076-3417/15/14/7721 |
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