Attention GANs: Unsupervised Deep Feature Learning for Aerial Scene Classification

Abstract

With the development of deep learning, supervised feature learning methods have achieved prominent performance in the field of aerial scene classification. However, supervised feature learning methods require a large amount of labeled training data. To address this limitation, in this article, a novel unsupervised deep feature learning method, namely, Attention generative adversarial networks (Attention GANs), is proposed for aerial scene classification. First, Attention GANs integrates the attention mechanism into GANs to enhance the representation power of the discriminator. Then, to obtain contextual information, a context-aggregation-based feature fusion architecture is designed in the discriminator. Furthermore, the generator and discriminator losses are improved on basis of the Relativistic GAN. At the same time, a content loss is formed by using the feature representations from the context-aggregation-based feature fusion architecture. In the experiments, our Attention GANs is evaluated via comprehensive experiments with four publicly available remote sensing scene data sets, i.e., the UC-Merced data set with 21 scene classes, the RSSCN7 data set with 7 scene classes, the AID data set with 30 scene classes, and the NWPU-RESISC45 data set with 45 scene classes. Experimental results demonstrate that our Attention GANs can obtain the best performance compared with the state-of-the-art methods.