Manifold Constraint Regularization for Remote Sensing Image Generation

Generative adversarial networks (GANs) have shown notable accomplishments in remote sensing (RS) domain. However, this article reveals that their performance on RS images falls short when compared to their impressive results with natural images. This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on RS images. To address this challenge, this article analyzes the characteristics of RS images and proposes manifold constraint regularization (MCR), a novel approach that tackles overfitting of GANs on RS images for the first time. Our method includes a new measure for evaluating the structure of the data manifold. Leveraging this measure, we propose the MCR term, which not only alleviates the overfitting problem, but also promotes alignment between the generated and real data manifolds, leading to enhanced quality in the generated images. The effectiveness and versatility of this method have been corroborated through extensive validation on various RS datasets and GAN models. The proposed method not only enhances the quality of the generated images, reflected in a 3.13% improvement in Fréchet inception distance (FID) score, but also boosts the performance of the GANs on downstream tasks, evidenced by a 3.76% increase in classification accuracy. The source code is available at https://github.com/rootSue/Manifold-RSGAN.