Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images

Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images

Blog Article

Remote sensing images are widely used in many applications.However, due to being limited by the sensors, it is difficult to obtain high-resolution (HR) Briefs images from remote sensing images.In this paper, we propose a novel unsupervised cross-domain super-resolution method devoted to reconstructing a low-resolution (LR) remote sensing image guided by an unpaired HR visible natural image.Therefore, an unsupervised visible image-guided remote sensing image super-resolution network (UVRSR) is built.

The network is divided into two learnable branches: a visible image-guided branch (VIG) and a remote sensing image-guided branch (RIG).As HR visible images can provide rich textures and sufficient high-frequency information, the purpose of VIG is to treat them as targets and make full use of their advantages in reconstruction.Specially, we first use a CycleGAN to drag the LR visible natural images to the remote sensing domain; then, we apply an SR network to upscale these simulated remote sensing domain LR images.However, the Canine - Grooming domain gap between SR remote sensing images and HR visible targets is massive.

To enforce domain consistency, we propose a novel domain-ruled discriminator in the reconstruction.Furthermore, inspired by the zero-shot super-resolution network (ZSSR) to explore the internal information of remote sensing images, we add a remote sensing domain inner study to train the SR network in RIG.Sufficient experimental works show UVRSR can achieve superior results with state-of-the-art unpaired and remote sensing SR methods on several challenging remote sensing image datasets.