Semi-Supervised Remote-Sensing Image Scene Classification Using Representation Consistency Siamese Network

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 14
• Main Authors: Miao, Wang, Geng, Jie, Jiang, Wen
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Artificial neural networks; Classification; Consistency; Convolutional neural networks; Datasets; Deep learning; Feature extraction; Generative adversarial networks; Image classification; Involution-generative adversarial network (GAN); Machine learning; Remote sensing; remote-sensing image; Representations; scene classification; semi-supervised learning (SSL); Semisupervised learning; Sensors; Siamese network; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3140485

Deep learning has achieved excellent performance in remote-sensing image scene classification, since a large number of datasets with annotations can be applied for training. However, in actual applications, there is just a few annotated samples and a large number of unannotated samples in remote-sensing images, which leads to overfitting of the deep model and affects the performance of scene classification. In order to address these problems, a semi-supervised representation consistency Siamese network (SS-RCSN) is proposed for remote-sensing image scene classification. First, considering intraclass diversity and interclass similarity of remote-sensing images, Involution-generative adversarial network (GAN) is utilized to extract the discriminative features from remote-sensing images via unsupervised learning. Then, Siamese network with a representation consistency loss is proposed for semi-supervised classification, which aims to reduce the differences of labeled and unlabeled data. Experimental results on UC Merced dataset, RESICS-45 dataset, aerial image dataset (AID), and RS dataset demonstrate that our method yields superior classification performance compared with other semi-supervised learning (SSL) methods.