Volume 11 Issue 3
Jun.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(3): 324-334
LI Ning, GUO Zhishun, WU Lin, et al. River-Net: A novel neural network model for extracting river channel based on Refined-Lee Kernel[J]. Journal of Radars, 2022, 11(3): 324–334. doi: 10.12000/JR21148
Citation: LI Ning, GUO Zhishun, WU Lin, et al. River-Net: A novel neural network model for extracting river channel based on Refined-Lee Kernel[J]. Journal of Radars, 2022, 11(3): 324–334. doi: 10.12000/JR21148
shu

River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel

DOI: 10.12000/JR21148
  • 1.

    College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

  • 2.

    Henan Engineering Research Center of Intelligent Technology and Application, Kaifeng 475004, China

  • 3.

    Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China

Funds:  Major Public Welfare Projects in Henan Province (201300311300), The Plan of Science and Technology of Henan Province (212102210101, 212102210093), The National Natural Science Foundation of China (61871175)
More Information
  • Corresponding author: WU Lin, henuwl@henu.edu.cn
  • Received Date: 2021-10-09
  • Accepted Date: 2021-12-08
  • Rev Recd Date: 2021-12-06
    • Available Online: 2021-12-14
  • Publish Date: 2021-12-27
    Abstract
  • 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.

     

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