基于深度学习的遥感图像水边线提取方法与应用

本文提出一种基于注意力机制的像素级海陆语义分割网络A-Unet用来提取水边线，并通过条件随机场对A-Unet分类结果进行细化。以天津沿海地区的人工海岸与威海地区的基岩海岸为例，对所提方法进行了验证，实验表明，与其他水边线分割方法相比，本文提出的方法能够得到更精细的结果，实现对遥感图像水边线的像素级语义分割。该方法提取了天津地区近10年海岸线并对其变化趋势进行了定性和定量分析，可为城市海洋资源的合理开发、海洋生态环境的保护提供更好的决策与参考依据。

A water edge extraction method based on deep learning for remote sensing images

The paper proposes a pixel-level sea-land semantic segmentation network A-Unet based on the attention mechanism to extract water edges. The network refines the A-Unet classification results through conditional random fields to realize the pixel-level semantics of remote sensing image water edges segmentation. Using the historical remote sensing images of Tianjin coastal area and Weihai area as data source, the paper extracts the coastline of Tianjin area of the past ten years and anaylses its change trendency qualitatively and quantitatively. Moreover, the natural waterline of Weihai area is employed to verify the accuracy of proposed model. Experiments show that compared with other waterfront segmentation methods, the proposed network can obtain more refined results, which can provide a better decision-making and reference basis for the rational development of urban marine resources and the protection of marine ecological environment.