Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913477164007424 |
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| author | Yang, Qiming Wang, Zixin Liu, Shinan Li, Zizheng |
| author_facet | Yang, Qiming Wang, Zixin Liu, Shinan Li, Zizheng |
| contents | In recent years, although U-Net network has made significant progress in the field of image segmentation, it still faces performance bottlenecks in remote sensing image segmentation. In this paper, we innovatively propose to introduce SimAM and CBAM attention mechanism in U-Net, and the experimental results show that after adding SimAM and CBAM modules alone, the model improves 17.41% and 12.23% in MIoU, and the Mpa and Accuracy are also significantly improved. And after fusing the two,the model performance jumps up to 19.11% in MIoU, and the Mpa and Accuracy are also improved by 16.38% and 14.8% respectively, showing excellent segmentation accuracy and visual effect with strong generalization ability and robustness. This study opens up a new path for remote sensing image segmentation technology and has important reference value for algorithm selection and improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm Yang, Qiming Wang, Zixin Liu, Shinan Li, Zizheng Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition In recent years, although U-Net network has made significant progress in the field of image segmentation, it still faces performance bottlenecks in remote sensing image segmentation. In this paper, we innovatively propose to introduce SimAM and CBAM attention mechanism in U-Net, and the experimental results show that after adding SimAM and CBAM modules alone, the model improves 17.41% and 12.23% in MIoU, and the Mpa and Accuracy are also significantly improved. And after fusing the two,the model performance jumps up to 19.11% in MIoU, and the Mpa and Accuracy are also improved by 16.38% and 14.8% respectively, showing excellent segmentation accuracy and visual effect with strong generalization ability and robustness. This study opens up a new path for remote sensing image segmentation technology and has important reference value for algorithm selection and improvement. |
| title | Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm |
| topic | Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12672 |