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.

中文翻译：

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高分辨率卫星图像中水体分割的多尺度细化网络

由于水体的外观、大小和形状的显着变化，高分辨率卫星图像中的水体分割具有挑战性。在这封信中，提出了一种用于水体分割的新型多尺度细化网络（MSR-Net）。与大多数基于学习的方法类似，MSR-Net 利用多尺度信息进行分割，但它在两个方面改进了现有网络：首先，它以新的视角使用多尺度信息。MSR-Net采用了一种新的多尺度细化方案，充分利用多尺度特征进行更准确的水体分割，而不是传统的将特征串联起来并在一个统一尺度上进行分割的一次性方式。此外，设计了一种新颖的擦除注意模块，用于在细化方案期间进行有效的特征嵌入。与其他最先进的语义分割方法（包括 U-Net、SegNet、DeepLabv3+ 和 ExFuse）相比，在高分图像数据集和 DeepGlobe 数据集上的实验证明了 MSR-Net 的优越性。