Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images
文献类型：期刊论文
| 作者 | Liu, Bingxue1,2; Wang, Wei2; Wu, Yuming2; Gao, Xing2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 16期号:23页码:4464 |
| 关键词 | remote sensing (RS) landslide segmentation attention mechanism spatial detailed information |
| DOI | 10.3390/rs16234464 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The development of artificial intelligence makes it possible to rapidly segment landslides. However, there are still some challenges in landslide segmentation based on remote sensing images, such as low segmentation accuracy, caused by similar features, inhomogeneous features, and blurred boundaries. To address these issues, we propose a novel deep learning model called AST-UNet in this paper. This model is based on structure of SwinUNet, attaching a channel Attention and spatial intersection (CASI) module as a parallel branch of the encoder, and a spatial detail enhancement (SDE) module in the skip connection. Specifically, (1) the spatial intersection module expands the spatial attention range, alleviating noise in the image and enhances the continuity of landslides in segmentation results; (2) the channel attention module refines the spatial attention weights by feature modeling in the channel dimension, improving the model's ability to differentiate targets that closely resemble landslides; and (3) the spatial detail enhancement module increases the accuracy for landslide boundaries by strengthening the attention of the decoder to detailed features. We use the landslide data from the area of Luding, Sichuan to conduct experiments. The comparative analyses with state-of-the-art (SOTA) models, including FCN, UNet, DeepLab V3+, TransFuse, TranUNet, and SwinUNet, prove the superiority of our AST-UNet for landslide segmentation. The generalization of our model is also verified in the experiments. The proposed AST-UNet obtains an F1-score of 90.14%, mIoU of 83.45%, foreground IoU of 70.81%, and Hausdorff distance of 3.73, respectively, on the experimental datasets. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001378187700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210438]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wu, Yuming |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Bingxue,Wang, Wei,Wu, Yuming,et al. Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images[J]. REMOTE SENSING,2024,16(23):4464. |
| APA | Liu, Bingxue,Wang, Wei,Wu, Yuming,&Gao, Xing.(2024).Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images.REMOTE SENSING,16(23),4464. |
| MLA | Liu, Bingxue,et al."Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images".REMOTE SENSING 16.23(2024):4464. |
入库方式： OAI收割
来源：地理科学与资源研究所
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