Semi-Supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

In the existing studies on remote sensing image scene classification, the supervised learning methods which are fine-tuned from pre-trained model require a large amount of labeled training data and parameters, while unsupervised learning methods do not make full use of label information, and the classification performance could be improved. In this paper, we introduced semi-supervised learning into generative adversarial network (GAN), so the discriminator learned more discriminative features from labeled data and unlabeled data. Moreover, the mixup data augmentation method was introduced into our classification model to augment the data and stabilized the training process. We carried out extensive experiments for both UC-Merced and NWPU-RESISC45 datasets with a 5-fold cross-validation protocol using a linear SVM as classifier. We trained the proposed method on UC-Merced dataset and achieve an average overall accuracy of 94.05% under 80% training ratio. When trained on NWPU-RESISC45 dataset, the proposed method reached an average overall accuracy of 83.12% and 92.78% under the training ratios of 20% and 80% respectively, which achieves the state-of-the-art deep learning methods without pre-training.