Semi-supervised convolutional generative adversarial network for hyperspectral image classification

To solve the problem of insufficient annotated samples in hyperspectral image classification, the semi-supervised convolutional generative adversarial network classification model is proposed in this study. The generative adversarial framework constructs an adversarial game, where the generator captures data distribution and generates fake samples, while the discriminator determines whether the input comes from generated or training data. In the proposed method, a deep three-dimensional (3D) convolutional neural network is used to generate the so-called fake cube samples and another 3D deep residual network is designed to discriminate the inputs. Furthermore, the generated samples, labelled and unlabelled samples are put into the discriminator for joint training, and the trained discriminator can determine the authenticity of the sample and the class label. This semi-supervised generative adversarial training strategy can effectively improve the generalisation capability of the deep residual network where the labelled samples are limited. Three widely used hyperspectral images are utilised to evaluate the classification performance of the proposed method: Indian Pines, Pavia University, and Salinas-A. The classification results reveal that the proposed model can improve the classification performance and achieve competitive results compared with the state-of-art methods, especially when there are few training samples.