Remote sensing image scene classification based on generative adversarial networks

ABSTRACT Scene classification of remote sensing images plays an important role in many remote sensing image applications. Training a good classifier needs a large number of training samples. The labeled samples are often scarce and difficult to obtain, and annotating a large number of samples is time-consuming. We propose a novel remote sensing image scene classification framework based on generative adversarial networks (GAN) in this paper. GAN can improve the generalization ability of machine learning network model. However, generating large-size images, especially high-resolution remote sensing images is difficult. To address this issue, the scaled exponential linear units (SELU) are applied into the GAN to generate high quality remote sensing images. Experiments carried out on two datasets show that our approach can obtain the state-of-the-art results compared with the classification results of the classic deep convolutional neural networks, especially when the number of training samples is small.