FWENet: a deep convolutional neural network for flood water body extraction based on SAR images
As one of the most severe natural disasters in the world, floods caused substantial economic losses and casualties every year. Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster rel...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2021.1995513 |
| _version_ | 1827811133285728256 |
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| author | Jingming Wang Shixin Wang Futao Wang Yi Zhou Zhenqing Wang Jianwan Ji Yibing Xiong Qing Zhao |
| author_facet | Jingming Wang Shixin Wang Futao Wang Yi Zhou Zhenqing Wang Jianwan Ji Yibing Xiong Qing Zhao |
| author_sort | Jingming Wang |
| collection | DOAJ |
| description | As one of the most severe natural disasters in the world, floods caused substantial economic losses and casualties every year. Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief. Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar (SAR) images and deep learning methods is relatively low, this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network (FWENet) for flood information extraction. Then three classical semantic segmentation models (UNet, Deeplab v3 and UNet++) and two traditional water body extraction methods (Otsu global thresholding method and Object-Oriented method) were compared with the FWENet model. Furthermore, this paper analyzed the water body area change situations of Poyang Lake. The main results of this paper were as follows: Compared with other five water body extraction methods, the FWENet model achieved the highest water body extraction accuracy, its F1 score and mean intersection over union (mIoU) were 0.9871 and 0.9808, respectively. This study could guarantee the subsequent research on flood extraction based on SAR images. |
| first_indexed | 2024-03-11T23:00:50Z |
| format | Article |
| id | doaj.art-bd07fcae52d44792ab08525d2ceb481f |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:50Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-bd07fcae52d44792ab08525d2ceb481f2023-09-21T14:57:10ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552022-12-0115134536110.1080/17538947.2021.19955131995513FWENet: a deep convolutional neural network for flood water body extraction based on SAR imagesJingming Wang0Shixin Wang1Futao Wang2Yi Zhou3Zhenqing Wang4Jianwan Ji5Yibing Xiong6Qing Zhao7Aerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAs one of the most severe natural disasters in the world, floods caused substantial economic losses and casualties every year. Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief. Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar (SAR) images and deep learning methods is relatively low, this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network (FWENet) for flood information extraction. Then three classical semantic segmentation models (UNet, Deeplab v3 and UNet++) and two traditional water body extraction methods (Otsu global thresholding method and Object-Oriented method) were compared with the FWENet model. Furthermore, this paper analyzed the water body area change situations of Poyang Lake. The main results of this paper were as follows: Compared with other five water body extraction methods, the FWENet model achieved the highest water body extraction accuracy, its F1 score and mean intersection over union (mIoU) were 0.9871 and 0.9808, respectively. This study could guarantee the subsequent research on flood extraction based on SAR images.http://dx.doi.org/10.1080/17538947.2021.1995513deep learningflood water body extractionsarpoyang lake |
| spellingShingle | Jingming Wang Shixin Wang Futao Wang Yi Zhou Zhenqing Wang Jianwan Ji Yibing Xiong Qing Zhao FWENet: a deep convolutional neural network for flood water body extraction based on SAR images International Journal of Digital Earth deep learning flood water body extraction sar poyang lake |
| title | FWENet: a deep convolutional neural network for flood water body extraction based on SAR images |
| title_full | FWENet: a deep convolutional neural network for flood water body extraction based on SAR images |
| title_fullStr | FWENet: a deep convolutional neural network for flood water body extraction based on SAR images |
| title_full_unstemmed | FWENet: a deep convolutional neural network for flood water body extraction based on SAR images |
| title_short | FWENet: a deep convolutional neural network for flood water body extraction based on SAR images |
| title_sort | fwenet a deep convolutional neural network for flood water body extraction based on sar images |
| topic | deep learning flood water body extraction sar poyang lake |
| url | http://dx.doi.org/10.1080/17538947.2021.1995513 |
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