Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification

We propose a three-player spectral generative adversarial network (GAN) architecture to afford GAN the ability to manage minority classes under imbalanced conditions. A class-dependent mixture generator spectral GAN (MGSGAN) was developed to force generated samples to remain within the actual distribution of the data. MGSGAN was able to generate minority classes, even when the imbalanced ratio of majority to minority classes was high. A classifier based on lower features was adopted along with a sequential discriminator to develop a three-player GAN game. The generative networks performed data augmentation to improve the classifier x2019; s performance. The proposed method was validated using two hyperspectral image data sets and compared with state-of-the-art methods in two class-imbalanced settings corresponding with real data distributions.