A Novel Contrastive Learning Model for Aerial Images

In the field of remote sensing, tasks related to images often require a large amount of manually annotated data for model training, thus requiring a large amount of human resources with professional knowledge. To alleviate this problem, some self-supervised methods have been proposed for learning image representations for downstream tasks. As a specific form, contrastive learning can be effectively used for unlabeled image data. Using this method, we achieve the ability to learn informative feature representations from extensive unlabeled data, thereby establishing a robust initialization model for subsequent tasks. But during the model learning process, the abundance and quality of negative samples significantly impact the model’s overall performance. Consequently, we improve the existing contrastive learning architecture of the conventional two-pathway network by incorporating an additional branch equipped with a hybrid encoder. This modification facilitates the fusion of both global and local features. Furthermore, we introduce other optimization techniques, including image reconstruction and indicator vector transformation. Notably, our approach emphasizes maintaining diversity among negative samples stored in the historical feature storage queue. Our model provides competitive results on aerial image dataset (AID) classification. Besides, our model outperforms other self-supervised baselines under different proportions of labeled data fine-tuning.