Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

Pollution caused by oil spills does irreversible harm to marine biosystems. To find maritime oil spills, Synthetic Aperture Radar (SAR) has emerged as a crucial mean. How to accurately distinguish oil spill areas from other types of areas is a committed step in detecting oil spills. Owing to its capacity to extract multiscale features and its distinctive decoder, the Deeplabv3+ framework has been developed into an excellent deep learning model in field of picture segmentation. However, in some SAR pictures, there is a lack of clarity in the segmentation of oil film edges and incorrect segmentation of small areas. In order to solve these problems, an improved network, named ASA-DRNet, has been proposed. Firstly, a new structure which combines an axial self-attention module with ResNet-18 is proposed as the backbone of DeepLabv3+ encoder. Secondly, a atrous spatial pyramid pooling (ASPP) module is optimized to improve the network’s capacity of extracting multiscale features and to increase the speed of model calculation and finally merging low-level features of different resolutions to enhance the competence of network to extract edge information. The experiments show that ASA-DRNet obtains the better results compared to other neural network models.