A generative adversarial network (GAN) usually contains a generative network and a
discriminative network in competition with each other. The GAN has shown its capability in a
variety of applications. In this paper, the usefulness and effectiveness of GAN for
classification of hyperspectral images (HSIs) are explored for the first time. In the proposed
GAN, a convolutional neural network (CNN) is designed to discriminate the inputs and
another CNN is used to generate so-called fake inputs. The aforementioned CNNs are …

In order to improve the classification accuracy of hyperspectral images, a novel
hyperspectral image classification method based on generative adversarial network is
proposed. The proposed generative adversarial network consists of generator, discriminator
and classifier, in which the generator is used to approximate the data distribution of
hyperspectral samples and generate specific categories of samples. The discriminator is a
binary classifier to determine whether the input samples are real data. And the classifier is …