Water-body surveying and mapping is of great significance for water resources utilization, flood monitoring, and environmental protection. However, due to distribution diversities, shape and size variations, and complex scenarios of water-bodies, it is still challengeable to accurately and efficiently extract water-bodies from high-resolution remotely sensed images. In this paper, we propose a multi-scale context extractor network, termed as MSCENet, for delineating water-bodies from high-resolution optical remotely sensed images. The MSCENet mainly contains three key parts: a multi-scale feature encoder, a feature decoder, and a context feature extractor module. To address shape and size variations of water-bodies, the Res2Net is used in the feature encoder to extract rich multi-scale information of water-bodies. The context extractor module is composed of an assorted dilated convolution unit and a complex multi-kernel pooling unit, which further extracts multi-scale contextual information to generate high-level feature maps. The robustness and effectiveness of our MSCENet have been evaluated on two public datasets: LandCover.ai Data Set and DeepGlobe Data Set. Comparative experiments indicate the superiority and applicability of the MSCENet in water-body extraction.

水体测绘对于水资源利用、洪水监测和环境保护具有重要意义。然而，由于水体的分布多样性、形状和尺寸变化以及复杂的场景，从高分辨率遥感图像中准确有效地提取水体仍然具有挑战性。在本文中，我们提出了一个多尺度上下文提取器网络，称为 MSCENet，用于从高分辨率光学遥感图像中描绘水体。MSCENet 主要包含三个关键部分：多尺度特征编码器、特征解码器和上下文特征提取器模块。为了解决水体的形状和大小变化，在特征编码器中使用 Res2Net 来提取丰富的水体多尺度信息。上下文提取器模块由一个分类的扩张卷积单元和一个复杂的多核池化单元组成，进一步提取多尺度上下文信息以生成高级特征图。我们的 MSCENet 的稳健性和有效性已经在两个公共数据集上进行了评估：LandCover.ai 数据集和 DeepGlobe 数据集。对比实验表明MSCENet在水体提取方面的优越性和适用性。