Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Abstract Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index ( NDWI ), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field.