SIC-TFRF: Sea Ice Classification with Textural Features and Random Forest

Sea ice cannot only have a significant impact on hydrological changes, climate systems, and energy balance on Earth, but it can also directly interfere with maritime activities, posing serious obstacles to ocean economic development, polar scientific research, and other activities. In today’s world, where global warming is accelerating the melting of polar sea ice, the ability to accurately identify and classify sea ice becomes particularly important. Compared with traditional statistical methods, sea ice classification methods based on machine learning and deep learning have the advantages of fast computing speed and low resource consumption. These methods require a large amount of labeled data as a driver. However, the direct labeling cost of Synthetic Aperture Radar (SAR) sea ice image datasets is high, and previous sea ice classification methods struggle to balance data processing costs with classification accuracy. To address this problem, this paper proposes a sea ice classification method based on textural features and random forest, which is called SIC-TFRF. Specifically, we first extract the textural features based on the gray-level co-occurrence matrix (GLCM) and then use the wrapper method to filter and integrate them into the polarization features of the SAR images, guiding the training of the random forest model. Extensive experiments are conducted to verify the superiority of our proposals. Particularly, the accuracy of the binary classification of sea ice and seawater and the multi-classification of different types of sea ice and seawater can reach 96.51% and 92.78%, respectively.