Manifold Constraint Regularization Improves Generative Adversarial Networks for Remote Sensing Image Generation

Generative Adversarial Networks (GANs) exhibit a heightened susceptibility to overfitting on remote sensing images compared to natural images. This study proposes a novel manifold constraint regularization (MCR) method that tackles the overfitting problem by encouraging the learned features to align with the structure of the real data manifold, promoting the discriminator to generalize well beyond the training data.