  In recent years, hyperspectral image (HSI) classification based on generative
adversarial networks (GAN) has achieved great progress. GAN-based
classification methods can mitigate the limited training sample dilemma to some
extent. However, several studies have pointed out that existing GAN-based HSI
classification methods are heavily affected by the imbalanced training data
problem. The discriminator in GAN always contradicts itself and tries to
associate fake labels to the minority-class samples, and thus impair the
classification performance. Another critical issue is the mode collapse in
GAN-based methods. The generator is only capable of producing samples within a
narrow scope of the data space, which severely hinders the advancement of
GAN-based HSI classification methods. In this paper, we proposed an Adaptive
DropBlock-enhanced Generative Adversarial Networks (ADGAN) for HSI
classification. First, to solve the imbalanced training data problem, we adjust
the discriminator to be a single classifier, and it will not contradict itself.
Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method
employed in the generator and discriminator to alleviate the mode collapse
issue. The AdapDrop generated drop masks with adaptive shapes instead of a
fixed size region, and it alleviates the limitations of DropBlock in dealing
with ground objects with various shapes. Experimental results on three HSI
datasets demonstrated that the proposed ADGAN achieved superior performance
over state-of-the-art GAN-based methods. Our codes are available at
https://github.com/summitgao/HC_ADGAN
