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.
Highlights
Sea ice is an important part of the earth's climate, and it is one of the indicators of global climate change
In order to verify the effectiveness of the algorithm proposed in this paper, traditional methods such as the minimum distance method, maximum likelihood method, Mahalanobis distance method and support vector machine method were selected as the comparison algorithms
From the sea ice images and the accuracy table (Table 1), we can see that for the sea water analysis, all of the algorithms can get a good results with an accuracy of over 80%
Summary
Sea ice is an important part of the earth's climate, and it is one of the indicators of global climate change. Traditional in situ methods such as ocean station observation and ice area survey are limited to time and space constraints, and it is difficult to meet the requirements of large-scale spatial information acquisition and dynamic monitoring of time series. Remote sensing can get large-scale data more quickly and with high accuracy, and has become one of the important methods on sea ice monitoring and analysing. The remote sensing data used for sea ice usually comes from SAR(Synthetic Aperture Radar), MODIS(Moderate Resolution Imaging Spectrometer), Landsat and Hyperspectral images. Ressel built a gray-level co-occurrence matrix based on TerraSAR-X data and proposed a framework for sea ice classification based on neural network[5, 6]. Very limited study use high-resolution multispectral satellites like Sentinel-2 to classify the sea ice. Deep learning is a new wave in the field of machine learning. The experimental results showed that the method proposed here have achieved better classification results compared with traditional classification algorithms
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