Abstract: The dynamic monitoring of coastline is of great significance to the planning and management of coastal zone. Due to the complex sea and land environment, the spectral characteristics of the sea and land boundary in remote sensing images are not obvious, which leads to inaccurate positioning of the extracted coastline. This paper proposes a deep convolutional neural network (EWNet) combining semantic segmentation network and edge detection network. The network contains two branch streams. The semantic segmentation stream is responsible for extracting hierarchical semantic information and is used to guide the edge detection stream to obtain coastline semantic information. The edge detection stream uses  the semantic segmentation stream to refine the edge semantic information. Experimental results on GF-1 remote sensing images show that, compared with several latest models, EWNet obtains more accurate coastline boundary extraction results.

Key words: coastline extraction, neural network, smantic segmentation, edge detection, remote sensing image