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

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.

Keywords

• synthetic aperture radar (sar)
• refined-lee kernel
• refined-lee filter
• neural network
• river channel extraction