River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel
High-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2022-06-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR21148 |
| _version_ | 1797422226062966784 |
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| author | Ning LI Zhishun GUO Lin WU Jianhui ZHAO |
| author_facet | Ning LI Zhishun GUO Lin WU Jianhui ZHAO |
| author_sort | Ning LI |
| collection | DOAJ |
| description | High-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering concept and the filtering characteristics of the convolution operation are combined, and an optimized internal weight convolution kernel Refined-Lee Kernel is proposed according to the geometric characteristics of the river channel. A novel river extraction deep neural network model, the River-Net, is also proposed. To verify the effectiveness of the proposed model, this article utilized 20 m resolution Interferometric Wideswath (IW) image data obtained from the European Space Agency Sentinel-1 satellite before and after the 20 July rainstorm in Zhengzhou, employing the images before the rainstorm to train the model. The model, after training, was used to extract the Yellow River channel and analyze the rise of the river after the rainstorm. Experimental results show that the proposed model can extract river channels from SAR images more accurately than trendy semantic segmentation models. The model has important application value for flood disaster detection and evaluation. |
| first_indexed | 2024-03-09T07:29:10Z |
| format | Article |
| id | doaj.art-56277a015e4b46d89d50fd3dba5de931 |
| institution | Directory Open Access Journal |
| issn | 2095-283X |
| language | English |
| last_indexed | 2024-03-09T07:29:10Z |
| publishDate | 2022-06-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-56277a015e4b46d89d50fd3dba5de9312023-12-03T06:42:57ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2022-06-0111332433410.12000/JR21148R21148River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee KernelNing LI0Zhishun GUO1Lin WU2Jianhui ZHAO3College of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaHigh-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering concept and the filtering characteristics of the convolution operation are combined, and an optimized internal weight convolution kernel Refined-Lee Kernel is proposed according to the geometric characteristics of the river channel. A novel river extraction deep neural network model, the River-Net, is also proposed. To verify the effectiveness of the proposed model, this article utilized 20 m resolution Interferometric Wideswath (IW) image data obtained from the European Space Agency Sentinel-1 satellite before and after the 20 July rainstorm in Zhengzhou, employing the images before the rainstorm to train the model. The model, after training, was used to extract the Yellow River channel and analyze the rise of the river after the rainstorm. Experimental results show that the proposed model can extract river channels from SAR images more accurately than trendy semantic segmentation models. The model has important application value for flood disaster detection and evaluation.https://radars.ac.cn/cn/article/doi/10.12000/JR21148synthetic aperture radar (sar)refined-lee kernelrefined-lee filterneural networkriver channel extraction |
| spellingShingle | Ning LI Zhishun GUO Lin WU Jianhui ZHAO River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel Leida xuebao synthetic aperture radar (sar) refined-lee kernel refined-lee filter neural network river channel extraction |
| title | River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel |
| title_full | River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel |
| title_fullStr | River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel |
| title_full_unstemmed | River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel |
| title_short | River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel |
| title_sort | river net a novel neural network model for extracting river channel based on refined lee kernel |
| topic | synthetic aperture radar (sar) refined-lee kernel refined-lee filter neural network river channel extraction |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR21148 |
| work_keys_str_mv | AT ningli rivernetanovelneuralnetworkmodelforextractingriverchannelbasedonrefinedleekernel AT zhishunguo rivernetanovelneuralnetworkmodelforextractingriverchannelbasedonrefinedleekernel AT linwu rivernetanovelneuralnetworkmodelforextractingriverchannelbasedonrefinedleekernel AT jianhuizhao rivernetanovelneuralnetworkmodelforextractingriverchannelbasedonrefinedleekernel |