An improved unsupervised representation learning generative adversarial network for remote sensing image scene classification

Unsupervised representation learning plays an important role in remote sensing image applications. Generative adversarial network (GAN) is the most popular unsupervised learning method in recent years. However, due to poor data augmentation, many GAN-based methods are often dif?cult to carry out. In this paper, we propose an improved unsupervised representation learning model called multi-layer feature fusion Wasserstein GAN (MF-WGANs) which considers extracting the feature information for remote sensing scene classification from unlabelled samples. First, we introduced a multi-feature fusion layer behind the discriminator to extract the high-level and mid-level feature information. Second, we combined the loss of multi-feature fusion layer and WGAN-GP to generate more stable and high-quality remote sensing images with a resolution of 256 x 256. Finally, the multi-layer perceptron classifier (MLP-classifier) is used to classify the features extracted from the multi-feature fusion layer and evaluated with the UC Merced Land-Use, AID and NWPU-RESISC45 data sets. Experiments show that MF-WGANs has richer data augmentation and better classification performance than other unsupervised representation learning classification models (e.g., MARTA GANs).