Abstract: Self-supervised learning can extract features from remote sensing images without relying on sample labels, but supervised method is still required to complete feature classification. To overcome the shortcomings of feature classification process and automatically classify remote sensing image features, an unsupervised semantic clustering method based on semi-supervised learning is proposed. First, the features of remote sensing images are extracted using self-supervised learning to abstract the high-level semantic information contained in the images. Then, each sample's closest neighbors are found based on the feature similarity, and a linear classifier is trained by clustering similar samples into one class using the online clustering method. Finally, based on the clustering results, pseudo labels are generated for samples with high confidence to construct a label sample set, and the model is fine-tuned using a semi-supervised method. Four public remote sensing image scene classification datasets, EuroSAT, GID, AID and NWPU-RESISC45, were validated, and the classification accuracy reached 94.84%, 63.55%, 76.42% and 86.24%, respectively. The method presented in this paper combines the advantages of online clustering and semi-supervised learning, alleviates the problems of error accumulation and insufficient sample utilization in existing methods. It makes full use of the self-supervised features to train the classification model and complete the scene classification of remote sensing images without using labeled samples at all. It achieves the classification effect close to supervised learning and has good application value.

Key words: remote sensing, scene classification, self-supervised, online clustering, semi-supervised