Deep contrastive learning based hybrid network for Typhoon intensity classification

The accurate classification of Typhoon Intensity (TI) based on cloud patterns in satellite images is crucial for effective disaster warning and management. However, this task is particularly challenging due to the striking visual similarities among different sub -classes of typhoons, coupled with a long-tailed distribution of class occurrences. Existing deep learning methods often struggle with biased classification stemming from imbalanced datasets and face difficulty discerning subtle differences between categories. This paper proposes a novel solution to these challenges through a hybrid framework that integrates contrastive learning and classifier learning. Contrastive learning is employed to increase the separation of similar sub -classes within the feature space, while classifier learning aims to train a discriminative and unbiased classifier. The proposed approach is evaluated extensively on the publicly available DeepTI dataset, demonstrating enhanced performance for both prominent and less frequent classes. The model achieves the 70.88% accuracy for seven categories typhoon intensity classification. The code implementation is at https://github.com/chen-huanxin/contrastive-hybridnetwork.