  We propose a three-player spectral generative adversarial network (GAN)
architecture to afford GAN with the ability to manage minority classes under
imbalance conditions. A class-dependent mixture generator spectral GAN (MGSGAN)
has been developed to force generated samples remain within the domain of the
actual distribution of the data. MGSGAN is able to generate minority classes
even when the imbalance ratio of majority to minority classes is high. A
classifier based on lower features is adopted with a sequential discriminator
to form a three-player GAN game. The generator networks perform data
augmentation to improve the classifier's performance. The proposed method has
been validated through two hyperspectral images datasets and compared with
state-of-the-art methods under two class-imbalance settings corresponding to
real data distributions.
