Advancing Weather Image Classification Using Deep Convolutional Neural Networks

Abstract

Automatic classification of weather images is a crucial task in the field of meteorology. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in image classification. In this paper, we propose an innovative CNN-based approach for weather image classification. Building upon prior research, our model capitalizes on the exceptional feature extraction capabilities of CNNs to accurately classify weather images across various categories. To enhance the generalization performance, we train the model using a combination of data augmentation techniques, including rotation, scaling and flipping. This paper introduces a CNN model specifically made for weather image classification, encompassing categories such as cloudy, sunny, rainy and snowy conditions. Through extensive training and evaluation on a substantial weather image dataset, our proposed model achieves an impressive accuracy rate of 98%. Our experimental results highlight the superior performance of our CNN model when compared to various state-of-the-art methods in weather image classification.