Locating Building Change via Adaptive Frequency Enhancement
Building change localization for very-high-resolution image is important in accurately tracking urbanization. However, the increase in resolution inevitably enhances the complex and variable background interference, which affects the accurate identification of building targets. This paper seeks to e...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10930904/ |
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| author | Lei Lu Yuejie Li Fei Yang Haixiong Li Guoqiang Wang Kun Xie |
| author_facet | Lei Lu Yuejie Li Fei Yang Haixiong Li Guoqiang Wang Kun Xie |
| author_sort | Lei Lu |
| collection | DOAJ |
| description | Building change localization for very-high-resolution image is important in accurately tracking urbanization. However, the increase in resolution inevitably enhances the complex and variable background interference, which affects the accurate identification of building targets. This paper seeks to explore a computational intelligence approach for tackling the above challenges. Specifically, a spatial frequency adaptive enhancement is presented that takes into account the spatial frequency difference of different land objects, so as to formulate the spatial frequency attention network. It can adaptively enhance the information that favors the detection of architectural changes and suppresses irrelevant background noise interference. The entire network is designed with a classic U-shaped architecture, and two attention schemes are specifically designed, including spatial frequency attention module to regulate spatial-frequency weights in each level of feature maps, and the triple attention gate to comprehensively integrate spatial, channel, and frequency information. Experiments on three popular real datasets show that our proposal is able to obtain advanced performance, and visualization of features indicates the positive effect of our spatial-frequency attention. |
| format | Article |
| id | doaj-art-14cabcfbeebd4827b21d3b87489e43f6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-14cabcfbeebd4827b21d3b87489e43f62025-08-20T02:49:20ZengIEEEIEEE Access2169-35362025-01-0113509805099510.1109/ACCESS.2025.355244410930904Locating Building Change via Adaptive Frequency EnhancementLei Lu0Yuejie Li1https://orcid.org/0000-0001-6517-9660Fei Yang2Haixiong Li3Guoqiang Wang4Kun Xie5https://orcid.org/0000-0003-4014-9646School of Information Engineering, Yulin University, Yulin, ChinaDepartment of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, ChinaSchool of Information Engineering, Yulin University, Yulin, ChinaSchool of Information Engineering, Yulin University, Yulin, ChinaSchool of Information Engineering, Yulin University, Yulin, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaBuilding change localization for very-high-resolution image is important in accurately tracking urbanization. However, the increase in resolution inevitably enhances the complex and variable background interference, which affects the accurate identification of building targets. This paper seeks to explore a computational intelligence approach for tackling the above challenges. Specifically, a spatial frequency adaptive enhancement is presented that takes into account the spatial frequency difference of different land objects, so as to formulate the spatial frequency attention network. It can adaptively enhance the information that favors the detection of architectural changes and suppresses irrelevant background noise interference. The entire network is designed with a classic U-shaped architecture, and two attention schemes are specifically designed, including spatial frequency attention module to regulate spatial-frequency weights in each level of feature maps, and the triple attention gate to comprehensively integrate spatial, channel, and frequency information. Experiments on three popular real datasets show that our proposal is able to obtain advanced performance, and visualization of features indicates the positive effect of our spatial-frequency attention.https://ieeexplore.ieee.org/document/10930904/Building change detectioncomputational intelligencefrequency attentionremote sensing images |
| spellingShingle | Lei Lu Yuejie Li Fei Yang Haixiong Li Guoqiang Wang Kun Xie Locating Building Change via Adaptive Frequency Enhancement IEEE Access Building change detection computational intelligence frequency attention remote sensing images |
| title | Locating Building Change via Adaptive Frequency Enhancement |
| title_full | Locating Building Change via Adaptive Frequency Enhancement |
| title_fullStr | Locating Building Change via Adaptive Frequency Enhancement |
| title_full_unstemmed | Locating Building Change via Adaptive Frequency Enhancement |
| title_short | Locating Building Change via Adaptive Frequency Enhancement |
| title_sort | locating building change via adaptive frequency enhancement |
| topic | Building change detection computational intelligence frequency attention remote sensing images |
| url | https://ieeexplore.ieee.org/document/10930904/ |
| work_keys_str_mv | AT leilu locatingbuildingchangeviaadaptivefrequencyenhancement AT yuejieli locatingbuildingchangeviaadaptivefrequencyenhancement AT feiyang locatingbuildingchangeviaadaptivefrequencyenhancement AT haixiongli locatingbuildingchangeviaadaptivefrequencyenhancement AT guoqiangwang locatingbuildingchangeviaadaptivefrequencyenhancement AT kunxie locatingbuildingchangeviaadaptivefrequencyenhancement |