Semi-Supervised DEGAN for Optical High-Resolution Remote Sensing Image Scene Classification

Abstract

Semi-supervised methods have made remarkable achievements via utilizing unlabeled samples for optical high-resolution remote sensing scene classification. However, the labeled data cannot be effectively combined with unlabeled data in the existing semi-supervised methods during model training. To address this issue, we present a semi-supervised optical high-resolution remote sensing scene classification method based on Diversity Enhanced Generative Adversarial Network (DEGAN), in which the supervised and unsupervised stages are deeply combined in the DEGAN training. Based on the unsupervised characteristic of the Generative Adversarial Network (GAN), a large number of unlabeled and labeled images are jointly employed to guide the generator to obtain a complete and accurate probability density space of fake images. The Diversity Enhanced Network (DEN) is designed to increase the diversity of generated images based on massive unlabeled data. Therefore, the discriminator is promoted to provide discriminative features by enhancing the generator given the game relationship between two models in DEGAN. Moreover, the conditional entropy is adopted to make full use of the information of unlabeled data during the discriminator training. Finally, the features extracted from the discriminator and VGGNet-16 are employed for scene classification. Experimental results on three large datasets demonstrate that the proposed scene classification method yields a superior classification performance compared with other semi-supervised methods.