Sea Ice Automatic Extraction in the Liaodong Bay from Sentinel-2 Imagery Using Convolutional Neural Networks

Abstract

Sea ice classification is one of the important tasks of sea ice monitoring. Accurate extraction of sea ice types is of great significance on sea ice conditions assessment, smooth navigation and safty marine operations. Sentinel-2 is an optical satellite launched by the European Space Agency. High spatial resolution and wide range imaging provide powerful support for sea ice monitoring. However, traditional supervised classification method is difficult to achieve fine results for small sample features. In order to solve the problem, this paper proposed a sea ice extraction method based on deep learning and it was applied to Liaodong Bay in Bohai Sea, China. The convolutional neural network was used to extract and classify the feature of the image from Sentinel-2. The results showed that the overall accuracy of the algorithm was 85.79% which presented a significant improvement compared with the tranditional algorithms, such as minimum distance method, maximum likelihood method, Mahalanobis distance method, and support vector machine method. The method proposed in this paper, which combines convolutional neural networks and high-resolution multispectral data, provides a new idea for remote sensing monitoring of sea ice.