Multiple Embeddings Contrastive Pretraining for Remote Sensing Image Classification

Abstract

This letter focuses on remote sensing image interpretation and aims to promote the use of contrastive self-supervised learning in varied applications of remote sensing image classification. The proposed method is a contrastive self-supervised pre-training framework that encourages the network to learn image representations by comparing image embeddings extracted by different encoders and predictors. Experiments were carried out on a variety of remote sensing image datasets to determine the efficacy of the proposed method for classification tasks. Results show that the proposed framework exploits the capabilities of encoders and outperforms the supervised learning method in terms of classification accuracy. Besides, it takes a few pre-training epochs to find a suboptimal initialization of network weights, and the pre-trained encoders use a little training data to get outstanding classification results, which shows the time and data efficiency of the proposed framework. Code is available at https://github.com/yinxu98/MECo.