LAUSR

Water-body segmentation in high-resolution satellite imagery is challenging because of the significant variations in the appearance, size, and shape of water bodies. In this letter, a novel multiscale refinement network (MSR-Net) is proposed for water-body segmentation. Similar to most learning-based methods, the MSR-Net resorts to the multiscale information for segmentation, but it improves existing networks in two ways: First, it uses the multiscale information in a new perspective. Instead of the traditional one-off manner that concatenates features and conducts segmentation on one uniform scale, the MSR-Net adopts a new multiscale refinement scheme that makes full use of the multiscale features for more accurate water-body segmentation. In addition, a novel erasing-attention module is designed for an effective feature embedding during the refinement scheme. Experiments on the Gaofen Image Data Set and the DeepGlobe Data Set demonstrate the superiority of MSR-Net when compared with the other state-of-the-art semantic segmentation methods, including U-Net, SegNet, DeepLabv3+, and ExFuse.