Hyperspectral remote sensing images have high spectral resolution and provide rich information on the types of features, but their high data dimensions and large data volume pose challenges in data processing. In addition, it is difficult to obtain ground truths of hyperspectral images (HSIs). Owing to the small number of training samples, the super-normative classification of HSIs is particularly challenging and time-consuming. As deep learning techniques continue to evolve, an increasing number of models have emerged for HSI classification. In this paper, we propose a classification algorithm for HSIs called the residual generative adversarial network (ResGAN), which automatically extracts spectral and spatial features for HSI classification. When unlabeled HSI data are used to train ResGAN, the generator generates fake HSI samples with a similar distribution to real data, and the discriminator contains high values suitable for training a small number of samples with real labels. The main innovations of this method are twofold. First, the generative adversarial network (GAN) is based on a dense residual network, which fully learns the higher-level features of HSIs. Second, the loss function is modified using the Wasserstein distance with a gradient penalty, and the discriminant model of the network is changed to enhance the training stability. Using image data obtained from airborne visible–infrared sensors of an imaging spectrometer, the performance of ResGAN was compared with that of two HSI classification methods. The proposed network obtains excellent classification results after only marking a small number of samples. From both subjective and objective viewpoints, ResGAN is an excellent alternative to the standard GAN for HSI classification.