A Novel Machine Learning Approach for Real-Time Correction of Atmospheric Distortion in Multispectral Satellite Images

An extensive study of atmospheric distortion correction in multispectral satellite images is presented in this work. Utilizing thorough testing on several datasets, assess the effectiveness of the suggested approach. The proposed methodology includes atmospheric modeling, preprocessing images, applying correction algorithms, and validation procedures. Examine the effects of different atmospheric elements, such as gases and aerosols, and use quantitative metrics like picture quality evaluations and cross-checks with real data. The new dehazing approach greatly improves the clarity and interpretability of multispectral satellite images by tackling the problems caused by haze-induced deterioration. The efficiency of the approach is seen in fields including forestry, urban planning, agriculture, and disaster relief. A better understanding of environmental dynamics facilitates informed decision-making for sustainable resource management. This work advances satellite image atmospheric correction techniques, which have ramifications for various earth observation uses.