Abstract
Clouds are one of the most common things in our daily life, and different types of clouds will foreshadow different weather conditions. Therefore, accurately identifying and classifying them are important for human forecasting the local weather. However, artificially classifying clouds could possibly cause a minor error. With the helping of machine learning classification model could avoid this situation as much as possible. In this study, our goal is to get sufficiently high accuracy of based-cloud classification. We used LBP as a preprocessing approach, and used KSVM, MLP, Custom Vision, and Resnet to compare each result. The dataset contains around 15 thousand cloud images in 5 types in the same format and pixels. With the implementation of four classification models, experiments showed that ResNet34 came out a great result with a test accuracy 94%. Therefore, this study demonstrates ResNet34 is a good image classification model in based-cloud classification field.
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