Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression
Water extraction from Synthetic Aperture Radar (SAR) images is crucial for water resource management and maintaining the sustainability of ecosystems. Though great progress has been achieved, there are still some challenges, such as an insufficient ability to extract water edge details, an inability...
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MDPI AG
2025-02-01
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| author | Meijun Gao Wenjie Dong Lifu Chen Zhongwu Wu |
| author_facet | Meijun Gao Wenjie Dong Lifu Chen Zhongwu Wu |
| author_sort | Meijun Gao |
| collection | DOAJ |
| description | Water extraction from Synthetic Aperture Radar (SAR) images is crucial for water resource management and maintaining the sustainability of ecosystems. Though great progress has been achieved, there are still some challenges, such as an insufficient ability to extract water edge details, an inability to detect small water bodies, and a weak ability to suppress background noise. To address these problems, we propose the Global Context Attention Feature Fusion Network (GCAFF-Net) in this article. It includes an encoder module for hierarchical feature extraction and a decoder module for merging multi-scale features. The encoder utilizes ResNet-101 as the backbone network to generate four-level features of different resolutions. In the middle-level feature fusion stage, the Attention Feature Fusion module (AFFM) is presented for multi-scale feature learning to improve the performance of fine water segmentation. In the advanced feature encoding stage, the Global Context Atrous Spatial Pyramid Pooling (GCASPP) is constructed to adaptively integrate the water information in SAR images from a global perspective, thereby enhancing the network’s ability to express water boundaries. In the decoder module, an attention modulation module (AMM) is introduced to rearrange the distribution of feature importance from the channel-space sequence perspective, so as to better extract the detailed features of water bodies. In the experiment, SAR images from Sentinel-1 system are utilized, and three different water areas with different features and scales are selected for independent testing. The Pixel Accuracy (PA) and Intersection over Union (IoU) values for water extraction are 95.24% and 91.63%, respectively. The results indicate that the network can extract more integral water edges and better detailed features, enhancing the accuracy and generalization of water body extraction. Compared with the several existing classical semantic segmentation models, GCAFF-Net embodies superior performance, which can also be used for typical target segmentation from SAR images. |
| format | Article |
| id | doaj-art-2492ab657a854f2ba4199915e9ec9821 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
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| series | Applied Sciences |
| spelling | doaj-art-2492ab657a854f2ba4199915e9ec98212025-08-20T02:04:34ZengMDPI AGApplied Sciences2076-34172025-02-01155236610.3390/app15052366Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise SuppressionMeijun Gao0Wenjie Dong1Lifu Chen2Zhongwu Wu3School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaWater extraction from Synthetic Aperture Radar (SAR) images is crucial for water resource management and maintaining the sustainability of ecosystems. Though great progress has been achieved, there are still some challenges, such as an insufficient ability to extract water edge details, an inability to detect small water bodies, and a weak ability to suppress background noise. To address these problems, we propose the Global Context Attention Feature Fusion Network (GCAFF-Net) in this article. It includes an encoder module for hierarchical feature extraction and a decoder module for merging multi-scale features. The encoder utilizes ResNet-101 as the backbone network to generate four-level features of different resolutions. In the middle-level feature fusion stage, the Attention Feature Fusion module (AFFM) is presented for multi-scale feature learning to improve the performance of fine water segmentation. In the advanced feature encoding stage, the Global Context Atrous Spatial Pyramid Pooling (GCASPP) is constructed to adaptively integrate the water information in SAR images from a global perspective, thereby enhancing the network’s ability to express water boundaries. In the decoder module, an attention modulation module (AMM) is introduced to rearrange the distribution of feature importance from the channel-space sequence perspective, so as to better extract the detailed features of water bodies. In the experiment, SAR images from Sentinel-1 system are utilized, and three different water areas with different features and scales are selected for independent testing. The Pixel Accuracy (PA) and Intersection over Union (IoU) values for water extraction are 95.24% and 91.63%, respectively. The results indicate that the network can extract more integral water edges and better detailed features, enhancing the accuracy and generalization of water body extraction. Compared with the several existing classical semantic segmentation models, GCAFF-Net embodies superior performance, which can also be used for typical target segmentation from SAR images.https://www.mdpi.com/2076-3417/15/5/2366SARwater segmentationneural networkattention mechanism |
| spellingShingle | Meijun Gao Wenjie Dong Lifu Chen Zhongwu Wu Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression Applied Sciences SAR water segmentation neural network attention mechanism |
| title | Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression |
| title_full | Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression |
| title_fullStr | Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression |
| title_full_unstemmed | Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression |
| title_short | Automatic Extraction of Water Body from SAR Images Considering Enhanced Feature Fusion and Noise Suppression |
| title_sort | automatic extraction of water body from sar images considering enhanced feature fusion and noise suppression |
| topic | SAR water segmentation neural network attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/5/2366 |
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