CALIPSO-Guided Dual-Branch Network With Spectral–Spatial Attention for Himawari-8 Sea Fog Detection

Sea fog poses a critical challenge to maritime safety. Current sea fog detection methods based on geostationary satellite data predominantly rely on the visual interpretation of samples, yet conventional visual interpretation methods are prone to subjective biases in sample labeling. This study proposes a novel dual-branch Patch-CNN architecture using Himawari-8 satellite data, which addresses labeling uncertainty through CALIPSO-derived vertical aerosol profiles that unambiguously discriminate sea fog from low-level clouds. Training samples were exclusively constructed from CALIPSO vertical feature masks (VFM), ensuring spectral reliability by eliminating cloud-fog ambiguity. The framework integrates three distinctive features: (1) Spatial-context preservation via patch-based inputs to encapsulate neighborhood characteristics around sample points; (2) multispectral feature extraction through a dual-branch architecture that separately captures characteristics from visible-near infrared and infrared channels; and (3) adaptive feature fusion employing attention-weighted feature integration to enhance discriminative representation. Evaluated across the Yellow-Bohai Sea region, the model achieved 89.74% overall accuracy and 82.98% detection probability, outperforming conventional machine learning and deep learning benchmarks. In validation testing with meteorological station data, the proposed method exhibited stronger robustness than traditional machine learning and deep learning methods, with 87 true positives, while case studies using CALIOP VFM and ICOADS visibility data found that the present network could accurately identify the foggy area. This work establishes a satellite-based fog detection network, particularly suited for complex marine environments where fog-cloud spectral overlap challenges traditional methods.