Generative Adversarial Networks (GANs) and their variants have achieved
remarkable success on natural images. However, their performance degrades when
applied to remote sensing (RS) images, and the discriminator often suffers from
the overfitting problem. In this paper, we examine the differences between
natural and RS images and find that the intrinsic dimensions of RS images are
much lower than those of natural images. As the discriminator is more
susceptible to overfitting on data with lower intrinsic dimension, it focuses
excessively on local characteristics of RS training data and disregards the
overall structure of the distribution, leading to a faulty generation model. In
respond, we propose a novel approach that leverages the real data manifold to
constrain the discriminator and enhance the model performance. Specifically, we
introduce a learnable information-theoretic measure to capture the real data
manifold. Building upon this measure, we propose manifold alignment
regularization, which mitigates the discriminator's overfitting and improves
the quality of generated samples. Moreover, we establish a unified GAN
framework for manifold alignment, applicable to both supervised and
unsupervised RS image generation tasks