HRPGAN: A GAN-based Model to Generate High-resolution Remote Sensing Images

Generative adversarial networks (GAN) has been mainly used in the generation of natural images such as MNIST, CIFAR10 as well as Imagenet datasets and achieves satisfying generation results. However, GAN always fails in generating high quality high-resolution remote sensing images because remote sensing images are large in size and have various ground objects. To address this issue, a novel framework called High-Resolution PatchGAN (HRPGAN) is introduced in this paper. The structure of HRPGAN follows PatchGAN, but the batch normalization layers are removed and the ReLU activation is replaced by the SELU activation. In addition, a new loss function consisting of the adversarial loss, perceptual reconstruction loss and regularization loss is used in HRPGAN. Experiment results show that the proposed HRPGAN model generates the more diverse and lifelike images in HR remote sensing generation than Bicubic method and TGAN model.