High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image

River water body identification plays an important role in flood monitoring, urban planning, Thus, it attracts more interests of studying and investigating, especially based on remote sensing technology, The traditional NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) methods are widely used, However, these methods need manual intervention to select the threshold, In order to achieve automatic water body recognition, deep learning methods, such as CNN, VGG, Unet etc., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.