Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery
文献类型：期刊论文
| 作者 | Chen, Wantai1,2,3,4; Zhou, Yinfei1,2,3,4; Li, Xiaofeng2,3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2025-05-01 |
| 卷号 | 139页码:12 |
| 关键词 | Synthetic aperture radar (SAR) Coastal inundation mapping Deep learning Flood index |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2025.104550 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | This study introduces a flood index-enhanced deep learning model for coastal inundation mapping leveraging dual-polarization and bitemporal Sentinel-1 synthetic aperture radar (SAR) imagery. The proposed model, FIE-Net, introduces two key innovations to improve flood mapping accuracy. First, it integrates the Ratio Image (RI), a primary flood index derived from bitemporal SAR data, which provides a reliable reference for precise inundation area delineation. The index offers a clear, flood-specific signal that enhances segmentation precision. Second, the model employs a dual-branch U-Net framework, augmented with specialized modules to enhance feature extraction and better integrate diverse data sources. This combination enables the model to handle complex flood scenarios more effectively, thereby boosting overall performance. Using data from the Copernicus Emergency Management Service, 16 pairs of representative SAR images from Madagascar's Tropical Cyclones 2018 AVA and 2023 Cheneso were collected under various terrain conditions. After semi-automatic labeling and cropping, 4350 sample pairs were processed, with 2784/696/870 used for model training/validation/testing. The proposed model achieved the highest Intersection over Union (IOU) of 79.44%, outperforming the state-ofthe-art models across all evaluation metrics. The experiments also demonstrate that each introduced innovation contributes to improved accuracy, with the flood index making the most significant impact. Furthermore, the model's applicability was further confirmed with three independent flooded areas (689 samples, not included in training) from the event Cheneso. IOUs in all three scenes consistently exceeded 75%, underscoring the model's reliability and robustness in real-world scenarios. |
| 资助项目 | Chinese Academy of Sciences (CAS)[XDB42000000] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001476629500001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/201754]  |
| 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.Qingdao Key Lab Artificial Intelligence Oceanog, Qingdao, Peoples R China 2.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Wantai,Zhou, Yinfei,Li, Xiaofeng. Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,139:12. |
| APA | Chen, Wantai,Zhou, Yinfei,&Li, Xiaofeng.(2025).Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,139,12. |
| MLA | Chen, Wantai,et al."Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 139(2025):12. |
入库方式： OAI收割
来源：海洋研究所
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