Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images
Remote sensing image semantic segmentation has extensive applications in land resource planning and smart cities. Due to the problems of unclear boundary segmentation and insufficient Semantic information of small targets in high-resolution remote sensing images, an improved network TU net based on...
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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/10930761/ |
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| author | Yang Xu Bin Cao Hui Lu |
| author_facet | Yang Xu Bin Cao Hui Lu |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | Remote sensing image semantic segmentation has extensive applications in land resource planning and smart cities. Due to the problems of unclear boundary segmentation and insufficient Semantic information of small targets in high-resolution remote sensing images, an improved network TU net based on U-net++ is proposed. Secondly, the attention aggregation module of the base Transformer is introduced to capture global contextual information, replacing the original multi-level skip connections of U-net++. A cross-window interaction module is designed, which significantly reduces computational complexity and achieves a lightweight model. Finally, a dynamic feature fusion block is designed at the end of the decoder to obtain multi-class and multi-scale Semantic information and enhance the final segmentation effect. TU-net conducted experiments on two datasets, where OA, mIoU, and mF1 scores were higher than mainstream models. The IoU and F1 scores of small-sized target cars in the Vaihingen dataset were 0.896 and 0.962, respectively, which were 5% and 15.8% higher than the suboptimal model; The IoU and F1 scores of the trees in the Potsdam dataset are 0.913 and 0.936, respectively, which are 6.3% and 4.3% higher than the suboptimal model. The experimental results show that the model can more accurately segment small-sized targets and target boundaries. |
| format | Article |
| id | doaj-art-d75addf8eddc472fb453c953c76e8b4b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-d75addf8eddc472fb453c953c76e8b4b2025-08-20T01:51:35ZengIEEEIEEE Access2169-35362025-01-0113558775588610.1109/ACCESS.2025.355258110930761Improved U-Net++ Semantic Segmentation Method for Remote Sensing ImagesYang Xu0https://orcid.org/0000-0003-2758-1880Bin Cao1Hui Lu2Guiyang Aluminum Magnesium Design and Research Institute Company Ltd., Guiyang, ChinaSchool of Big Data and Information Engineering, Guizhou University, Guiyang, ChinaGuiyang Aluminum Magnesium Design and Research Institute Company Ltd., Guiyang, ChinaRemote sensing image semantic segmentation has extensive applications in land resource planning and smart cities. Due to the problems of unclear boundary segmentation and insufficient Semantic information of small targets in high-resolution remote sensing images, an improved network TU net based on U-net++ is proposed. Secondly, the attention aggregation module of the base Transformer is introduced to capture global contextual information, replacing the original multi-level skip connections of U-net++. A cross-window interaction module is designed, which significantly reduces computational complexity and achieves a lightweight model. Finally, a dynamic feature fusion block is designed at the end of the decoder to obtain multi-class and multi-scale Semantic information and enhance the final segmentation effect. TU-net conducted experiments on two datasets, where OA, mIoU, and mF1 scores were higher than mainstream models. The IoU and F1 scores of small-sized target cars in the Vaihingen dataset were 0.896 and 0.962, respectively, which were 5% and 15.8% higher than the suboptimal model; The IoU and F1 scores of the trees in the Potsdam dataset are 0.913 and 0.936, respectively, which are 6.3% and 4.3% higher than the suboptimal model. The experimental results show that the model can more accurately segment small-sized targets and target boundaries.https://ieeexplore.ieee.org/document/10930761/High resolution remote sensing imagesemantic segmentationfeature head refinement modulelightweightingdynamic feature fusion block |
| spellingShingle | Yang Xu Bin Cao Hui Lu Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images IEEE Access High resolution remote sensing image semantic segmentation feature head refinement module lightweighting dynamic feature fusion block |
| title | Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images |
| title_full | Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images |
| title_fullStr | Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images |
| title_full_unstemmed | Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images |
| title_short | Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images |
| title_sort | improved u net semantic segmentation method for remote sensing images |
| topic | High resolution remote sensing image semantic segmentation feature head refinement module lightweighting dynamic feature fusion block |
| url | https://ieeexplore.ieee.org/document/10930761/ |
| work_keys_str_mv | AT yangxu improvedunetsemanticsegmentationmethodforremotesensingimages AT bincao improvedunetsemanticsegmentationmethodforremotesensingimages AT huilu improvedunetsemanticsegmentationmethodforremotesensingimages |