Hyperspectral remote sensing image classification based on residual generative Adversarial Neural Networks

•A hyperspectral image classification model with an improved generative adversarial network is proposed as a solution to the problem that conventional hyperspectral image classification methods necessitate a large number of labelled samples for model training and have poor classification accuracy.•Compared with the original generator composed of 4 layers of deconvolution layers, this paper uses a 6-layer residual network composed of upsampling layers and convolution layers to replace the generator network structure to improve the generation ability of target data.•Compared with the original 5-layer convolutional layer discriminator, this paper uses an 18-layer residual convolutional network to replace the convolutional layer network structure of the discriminator and improve the feature extraction capability of the algorithm.