Nighttime sea fog recognition based on remote sensing satellite and deep neural decision tree

Remote sensing satellites have the characteristics of wide coverage and continuous observation, and are widely used in research related to the sea fog identification. Firstly, the Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP), which is capable of penetrating clouds and obtaining atmospheric profiles, was used to annotate medium and high cloud, low cloud, sea fog, and clear sky sea surface samples. Then, bright temperature features and texture features were extracted from each type of sample in combination with multi-channel data from the Himawari-8 satellite. Finally, according to the needs of sea fog monitoring, the inference decision tree for sea fog monitoring was abstracted and a deep neural decision tree model was built accordingly, which could achieve high accuracy for nighttime sea fog monitoring while having strong interpretability. The continuous observation data of Himawari-8 on the night of June 5, 2020 was selected to test the sea fog. The monitoring results can clearly show the dynamic development process of the sea fog events. At the same time, the sea fog monitoring method in this paper has an average probability of detection (POD) of 87.32%, an average false alarm ratio (FAR) of 13.19%, and an average critical success index (CSI) of 77.36%, which provides a new method for disaster prevention and mitigation of heavy fog at sea.