Semi-supervised remote sensing image scene classification with prototype-based consistency

Deep learning significantly improves the accuracy of remote sensing image scene classification, benefiting from the large-scale datasets. However, annotating the remote sensing images is time-consuming and even tough for experts. Deep neural networks trained using a few labeled samples usually generalize less to new unseen images. In this paper, we propose a semi-supervised approach for remote sensing image scene classification based on the prototype-based consistency, by exploring massive unlabeled images. To this end, we, first, propose a feature enhancement module to extract discriminative features. This is achieved by focusing the model on the foreground areas. Then, the prototype-based classifier is introduced to the framework, which is used to acquire consistent feature representations. We conduct a series of experiments on NWPU-RESISC45 and Aerial Image Dataset (AID). Our method improves the State-Of-The-Art (SOTA) method on NWPU-RESISC45 from 92.03% to 93.08% and on AID from 94.25% to 95.24% in terms of accuracy.