Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance.

Collecting ground truth labels for hyperspectral image classification is difficult and time-consuming. Without an adequate number of training samples, hyperspectral image (HSI) classification is a challenging problem. Using generative adversarial networks (GANs) is a promising technique for solving this problem because GANs can learn features from both labeled and unlabeled samples.