A TWO-STAGE CONTRASTIVE LEARNING FRAMEWORK FOR IMBALANCED AERIAL SCENE RECOGNITION

In real-world scenarios, aerial image datasets are generally class imbalanced, where the majority classes have rich samples, while the minority classes only have a few samples. Such class imbalanced datasets bring great challenges to aerial scene recognition. In this paper, we explore a novel two-stage contrastive learning framework, which aims to take care of representation learning and classifier learning, thereby boosting aerial scene recognition. Specifically, in the representation learning stage, we design a data augmentation policy to improve the potential of contrastive learning according to the characteristics of aerial images. And we employ supervised contrastive learning to learn the association between aerial images of the same scene. In the classification learning stage, we fix the encoder to maintain good representation and use the re-balancing strategy to train a less biased classifier. A variety of experimental results on the imbalanced aerial image datasets show the advantages of the proposed two-stage contrastive learning framework for the imbalanced aerial scene recognition.