Enhanced Seafog Detection Using Deep Learning and Contrastive Learning: Integrating Spectral and Motion Features

Seafog detection is a complex challenge in meteorology, primarily due to the extensive distribution of seafog and the limited availability of maritime observation stations. Remote sensing meteorological satellites, with their wide-area observation capabilities and abundant data resources, hold significant potential for seafog detection. However, current seafog detection approaches—whether based on traditional thresholding techniques or deep learning methods—do not fully exploit the multichannel information provided by remote sensing data, nor do they effectively capture the physical motion differences between seafog and other cloud types. Deep learning has demonstrated notable performance in seafog detection tasks, yet traditional threshold-based methods for seafog detection still hold the advantage of strong physical interpretability. Furthermore, challenges such as fuzzy seafog boundaries and overlap with clouds lead to discontinuous and incomplete detection regions, posing significant challenges for practical applications. To address these issues, we propose a deep learning approach that integrates the spectral and motion physical characteristics of seafog and various cloud types, enhancing the feature distinctions between seafog and other clouds to enable more accurate and interpretable feature extraction. To further optimize feature extraction, we propose a contrastive learning mechanism for seafog, aimed at amplifying the distinctions between seafog and other cloud types while simultaneously reducing intraclass variability within seafog across different conditions. Finally, we propose a probabilistic mask representation, which effectively mitigates the issue of regional discontinuities in detection, thereby enhancing the overall performance of the detection process. Our extensive experiments on the FY-4A satellite dataset demonstrate a CSI of 64.98%, which is 7.41% higher than the current best-performing method.