A New Deep Learning Method for Sea Fog Detection Using Fengyun-4A Satellite Data

Sea fog is a common maritime meteorological hazard that poses a serious threat to maritime traffic safety and coastal economic activities. Existing sea fog detection models still struggle to effectively capture both global structural information and local textural details, while spectral confusion between clouds and fog further limits their performance. To address these challenges, a daytime sea fog detection model, HA-ENASnet, is developed using data from China’s Fengyun-4A (FY-4A) satellite and compared with six representative semantic segmentation models. Among the seven models, HA-ENASnet achieves the best performance, followed by SegMamba, scSELinkNet, SegMAN, ECATransUnet, DeepLabv3+, and UNet++. In terms of Intersection over Union (IoU), HA-ENASnet outperforms SegMamba and UNet++ by 3. 08 and 17. 26 percentage points, respectively. Ablation experiments for this model demonstrate that the introduction of a collaborative mechanism combining local and global linear attention effectively enhances the model’s perception capabilities for sea fog regions. Furthermore, the combination of Adaptive Architecture Search (NAS) and Efficient Channel Attention (ECA) facilitates the adaptive fusion of spectral and spatial multiscale features, thereby improving the model’s ability to distinguish between cloud and fog in spectrally confounded regions. Additionally, the proposed model is validated on Himawari-8 satellite data, exhibiting satisfactory detection performance and generalization capabilities. This provides methodological support for sea fog remote sensing monitoring using the FY satellite series.