  Generative adversarial networks (GANs) have achieved remarkable progress in
the natural image field. However, when applying GANs in the remote sensing (RS)
image generation task, an extraordinary phenomenon is observed: the GAN model
is more sensitive to the size of training data for RS image generation than for
natural image generation. In other words, the generation quality of RS images
will change significantly with the number of training categories or samples per
category. In this paper, we first analyze this phenomenon from two kinds of toy
experiments and conclude that the amount of feature information contained in
the GAN model decreases with reduced training data. Then we establish a
structural causal model (SCM) of the data generation process and interpret the
generated data as the counterfactuals. Based on this SCM, we theoretically
prove that the quality of generated images is positively correlated with the
amount of feature information. This provides insights for enriching the feature
information learned by the GAN model during training. Consequently, we propose
two innovative adjustment schemes, namely Uniformity Regularization (UR) and
Entropy Regularization (ER), to increase the information learned by the GAN
model at the distributional and sample levels, respectively. We theoretically
and empirically demonstrate the effectiveness and versatility of our methods.
Extensive experiments on three RS datasets and two natural datasets show that
our methods outperform the well-established models on RS image generation
tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN.
