A multi-branch u-net for water area segmentation with multi-modality remote sensing images

Water area segmentation in remote sensing images is of great importance for flood monitoring. Convolutional neural networks have been successfully applied to various computer vision tasks. Among them, a U-shaped CNN known as U-Net achieves state-of-the-art performance on various types of image segmentation, including remote sensing images. However, there are still some difficulties in the water area segmentation of remote sensing images, such as complex backgrounds, cloud shading, and rough edges. In this work, we propose a multi-branch fusion U-Net (MFU-Net) method for water area segmentation with multi-modality remote sensing images. The experimental results showed that our MFU-Net can effectively and efficiently segment water area from Sentinel-1 and Sentinel-2 images, which F1, IoU and PA on the Sen1Floods11 dataset are 91.462%, 84.598% and 98.123%, respectively.