Remote Sensing Image Scene Classification with Self-Supervised Learning Based on Partially Unlabeled Datasets

In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted labeled datasets, which leads to a high cost of obtaining annotated samples. Self-supervised learning can alleviate this problem by using unlabeled data to learn the image's feature representation and then migrate to the downstream task. In this study, we use an encoder-decoder structure to construct a self-supervised learning architecture. In the encoding stage, the image mask is used to discard some of the image patches randomly, and the image's feature representation can be learned from the remaining image patches. In the decoding stage, the lightweight decoder is used to recover the pixels of the original image patches according to the features learned in the encoding stage. We constructed a large-scale unlabeled training set using several public scene classification datasets and Gaofen-2 satellite data to train the self-supervised learning model. In the downstream task, we use the encoder structure with the masked image patches that have been removed as the backbone network of the scene classification task. Then, we fine-tune the pre-trained weights of self-supervised learning in the encoding stage on two open datasets with complex scene categories. The datasets include NWPU-RESISC45 and AID. Compared with other mainstream supervised learning methods and self-supervised learning methods, our proposed method has better performance than the most state-of-the-art methods in the task of remote sensing image scene classification.