Water Body Recognition by Remote Sensing Images based on CBAM and Unet

In order to solve these problems, this paper used Unet deep learning technology to introduce the attention mechanism CBAM(Convolutional Block Attention Module)to dynamically capture the key feature information of the image, and adaptively adjust the attention weight according to the importance of each channel to enhance the expressive ability and performance of the water body recognition model.Through experimental verification, compared with the Unet water body recognition model, the river recognized by the CBAM+Unet water body recognition model was closer to the real river in width and direction and contour. The river edge recognition was much better. The accuracy, precision, recall, F1 value and Kappa coefficient of the model reach 98.24%, 98.73%, 99.32%, 99.02% and 89.77%, respectively,and the Kappa coefficient was increased by 8.52% compared with Unet, indicating that the CBAM+Unet water body recognition model indicated higher recognition accuracy and water edge extraction ability.