Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images
文献类型：期刊论文
| 作者 | Ren, Yibin4,5; Li, Xiaofeng4,5; Yang, Xiaofeng1,3; Xu, Huan2 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2022 |
| 卷号 | 19页码:5 |
| ISSN号 | 1545-598X |
| 关键词 | Sea ice Radar polarimetry Feature extraction Decoding Oceans Kernel Image segmentation Dual-attention sea ice and open water classification synthetic aperture radar (SAR) image U-Net |
| DOI | 10.1109/LGRS.2021.3058049 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | This study develops a deep learning (DL) model to classify the sea ice and open water from synthetic aperture radar (SAR) images. We use the U-Net, a well-known fully convolutional network (FCN) for pixel-level segmentation, as the model backbone. We employ a DL-based feature extracting model, ResNet-34, as the encoder of the U-Net. To achieve high accuracy classifications, we integrate the dual-attention mechanism into the original U-Net to improve the feature representations, forming a dual-attention U-Net model (DAU-Net). The SAR images are obtained from Sentinel-1A. The dual-polarized information and the incident angle of SAR images are model inputs. We used 15 dual-polarized images acquired near the Bering Sea to train the model and employ the other three images to test the model. Experiments show that the DAU-Net could achieve pixel-level classification; the dual-attention mechanism can improve the classification accuracy. Compared with the original U-Net, DAU-Net improves the intersection over union (IoU) by 7.48.% points, 0.96.% points, and 0.83.% points on three test images. Compared with the recently published model DenseNetFCN, the three improvement IoU values of DAU-Net are 3.04.% points, 2.53.% points, and 2.26.% points, respectively. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42040401] ; China Postdoctoral Science Foundation[2019M662452] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of Center for Ocean Mega-Science, CAS[COMS2019R02] ; CAS Program[Y9KY04101L] ; National Natural Science Foundation of China[41776183] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000732885500001 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/177569]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China 2.Jiangsu Ocean Univ, Sch Geomat & Marine Informat, Lianyungang 222005, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ren, Yibin,Li, Xiaofeng,Yang, Xiaofeng,et al. Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
| APA | Ren, Yibin,Li, Xiaofeng,Yang, Xiaofeng,&Xu, Huan.(2022).Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
| MLA | Ren, Yibin,et al."Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
入库方式： OAI收割
来源：海洋研究所
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