Multi-Satellite Image Matching and Deep Learning Segmentation for Detection of Daytime Sea Fog Using GK2A AMI and GK2B GOCI-II

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products.