Evaluating Multimodal Techniques for Predicting Visibility in the Atmosphere Using Satellite Images and Environmental Data

Abstract

Visibility is a measure of the atmospheric transparency at an observation point, expressed as the maximum horizontal distance over which a person can see and identify objects. Low atmospheric visibility often occurs in conjunction with air pollution, posing hazards to both traffic safety and human health. In this study, we combined satellite remote sensing images with environmental data to explore the classification performance of two distinct multimodal data processing techniques. The first approach involves developing four multimodal data classification models using deep learning. The second approach integrates deep learning and machine learning to create twelve multimodal data classifiers. Based on the results of a five-fold cross-validation experiment, the inclusion of various environmental data significantly enhances the classification performance of satellite imagery. Specifically, the test accuracy increased from 0.880 to 0.903 when using the deep learning multimodal fusion technique. Furthermore, when combining deep learning and machine learning for multimodal data processing, the test accuracy improved even further, reaching 0.978. Notably, weather conditions, as part of the environmental data, play a crucial role in enhancing visibility prediction performance.