A Semi-Supervised Siamese Network with Label Fusion for Remote Sensing Image Scene Classification

Abstract

Remote sensing image scene classification, which requires large amounts of labeled data, plays a critical role in a range of fields.However, in the actual complex environment, the obtained remote sensing images are sometimes unlabeled due to data perturbation and the cost of manual labeling, which limits the training effect and generalization ability. To solve this issue, a semi-supervised siamese network with label fusion is proposed for remote sensing image scene classification. The siamese network is developed to extract features from remote sensing image, where loss function based on the low-entropy principle is constructed to select the unlabeled data as pseudo-label samples. The labeled and pseudo-label samples are mixed to further train the siamese network. The results on UC Merced dataset and WHU-RS19 show that our method is capable to achieve excellent performance compared with other semi-supervised learning methods.