Abstract
Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.
Highlights
Sea ice refers to saltwater ice that is directly frozen from seawater, and includes continental glaciers, river ice, and lake ice that enter the ocean
Based on a GF1 image acquired on 12 January 2018, 800 sea ice and seawater sample points were selected, and the sea ice spectral information index was used to plot the distribution ranges of the different types of sea ice and seawater reflectance values (Figure 10)
The results revealed that after the sea ice spectral information index was constructed, the reflectance values of the different types of sea ice were larger, while the reflectance values of the seawater were concentrated within a small interval, indicating that the sea ice spectral information index can effectively extract the sea ice spectral information
Summary
Sea ice refers to saltwater ice that is directly frozen from seawater, and includes continental glaciers (icebergs and Iceland), river ice, and lake ice that enter the ocean. Sea ice greatly inhibits the heat and steam exchange between the ocean and atmosphere and alters the radiation budget and energy balance of the ocean’s surface. These changes have an important impact on oceanic hydrological conditions, atmospheric circulation, and ocean climate [1]. The production and disappearance of sea ice greatly affects human marine activities. Sea ice significantly affects the development of marine resources and marine transportation [2]. Sea ice presents a potential freshwater resource [3]. Accurate real-time monitoring of sea ice bears an important application value and theoretical significance
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