A Study on the Multi-Source Remote Sensing Visibility Classification Method Based on the LF-Transformer

Visibility is a critical meteorological factor for ensuring the safety of maritime and bridge transportation, and accurate identification of low-visibility levels is essential for early warning and operational scheduling. Traditional methods such as Random Forest often exhibit insufficient feature-modeling capability when dealing with high-dimensional, multi-source remote sensing data. Meanwhile, satellite observations used for visibility recognition are characterized by strong inter-channel correlations, complex nonlinear interactions, significant observational noise and outliers, and the scarcity of low-visibility samples that are easily confused with low clouds and haze. As a result, existing general deep learning methods (e.g., the Saint model) may still exhibit unstable attention weights and limited generalization under complex meteorological conditions. To address these limitations, this study constructs a visibility classification task for the Jiaxing–Shaoxing Cross-Sea Bridge region in China based on multi-channel visible and infrared spectral observations from the Fengyun-4A (FY-4A) and Fengyun-4B (FY-4B) satellites. We propose a visibility classification method using the LF-Transformer for the Jiaxing–Shaoxing Cross-Sea Bridge region in China, and systematically compare it with the Random Forest and Saint models. Experimental results show that the Precision of the LF-Transformer increases significantly from 0.47 (Random Forest) to 0.59, achieving a 13% improvement and demonstrating stronger discriminative ability and stability under complex meteorological conditions. Furthermore, a combination input of FY4A+FY4B outperform the single FY4A, with a 25.5% increased Macro F1-score. With an additional ensemble strategy, the LF-Transformer further improves its precision on the FY4A+FY4B fused dataset to 0.61, a 3% compared to the original LF-Transformer, indicating enhanced prediction stability. Overall, the proposed method substantially strengthens visibility classification performance and highlights the strong application potential of the LF-Transformer in remote-sensing-based meteorological tasks, particularly for low-visibility monitoring, early warning, and transportation safety assurance.