KMS National Astronomical Observatories, CAS
| Multi-Category Segmentation of Sentinel-2 Images Based on the Swin UNet Method | |
| Yao, Junyuan1,2; Jin, Shuanggen2,3 | |
| 2022-07-01 | |
| Source Publication | REMOTE SENSING
 |
| Volume | 14Issue:14Pages:15 |
| Abstract | Medium-resolution remote sensing satellites have provided a large amount of long time series and full coverage data for Earth surface monitoring. However, the different objects may have similar spectral values and the same objects may have different spectral values, which makes it difficult to improve the classification accuracy. Semantic segmentation of remote sensing images is greatly facilitated via deep learning methods. For medium-resolution remote sensing images, the convolutional neural network-based model does not achieve good results due to its limited field of perception. The fast-emerging vision transformer method with self-attentively capturing global features well provides a new solution for medium-resolution remote sensing image segmentation. In this paper, a new multi-class segmentation method is proposed for medium-resolution remote sensing images based on the improved Swin UNet model as a pure transformer model and a new pre-processing, and the image enhancement method and spectral selection module are designed to achieve better accuracy. Finally, 10-categories segmentation is conducted with 10-m resolution Sentinel-2 MSI (Multi-Spectral Imager) images, which is compared with other traditional convolutional neural network-based models (DeepLabV3+ and U-Net with different backbone networks, including VGG, ResNet50, MobileNet, and Xception) with the same sample data, and results show higher Mean Intersection Over Union (MIOU) (72.06%) and better accuracy (89.77%) performance. The vision transformer method has great potential for medium-resolution remote sensing image segmentation tasks. |
| Keyword | Swin UNet Swin Transformer remote sensing semantic segmentation Sentinel-2 |
| Funding Organization | Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project |
| DOI | 10.3390/rs14143382 |
| WOS Keyword | WATER |
| Language | 英语 |
| Funding Project | Strategic Priority Research Program Project of the Chinese Academy of Sciences[XDA23040100] ; Jiangsu Natural Resources Development Special Project[JSZRHYKJ202002] |
| Funding Organization | Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Jiangsu Natural Resources Development Special Project ; Jiangsu Natural Resources Development Special Project |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000832139000001 |
| Publisher | MDPI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.bao.ac.cn/handle/114a11/87919 |
| Collection | 中国科学院国家天文台 |
| Corresponding Author | Jin, Shuanggen |
| Affiliation | 1.Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200444, Peoples R China 2.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China 3.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China |
| Recommended Citation GB/T 7714 | Yao, Junyuan,Jin, Shuanggen. Multi-Category Segmentation of Sentinel-2 Images Based on the Swin UNet Method[J]. REMOTE SENSING,2022,14(14):15. |
| APA | Yao, Junyuan,&Jin, Shuanggen.(2022).Multi-Category Segmentation of Sentinel-2 Images Based on the Swin UNet Method.REMOTE SENSING,14(14),15. |
| MLA | Yao, Junyuan,et al."Multi-Category Segmentation of Sentinel-2 Images Based on the Swin UNet Method".REMOTE SENSING 14.14(2022):15. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment