RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image

Remote sensing image super-resolution (SR) plays an important role by supplementing the lack of original high-resolution (HR) images in the study scenarios of large spatial areas or long time series. However, due to the lack of imagery information in low-resolution (LR) images, single-image super-resolution (SISR) is an inherently ill-posed problem. Especially, it is difficult to reconstruct the fine textures of HR images at large upscaling factors (e.g., four times). In this work, based on Google Earth HR images, we explore the potential of the reference-based super-resolution (RefSR) method on remote sensing images, utilizing rich texture information from HR reference (Ref) images to reconstruct the details in LR images. This method can use existing HR images to help reconstruct the LR images of long time series or a specific time. We build a reference-based remote sensing SR data set (RRSSRD). Furthermore, by adopting the generative adversarial network (GAN), we propose a novel end-to-end reference-based remote sensing GAN (RRSGAN) for SR. RRSGAN can extract the Ref features and align them to the LR features. Eventually, the texture information in the Ref features can be transferred to the reconstructed HR images. In contrast to the existing RefSR methods, we propose a gradient-assisted feature alignment method that adopts the deformable convolutions to align the Ref and LR features and a relevance attention module (RAM) to improve the robustness of the model in different scenarios (e.g., land cover changes and cloud coverage). The experimental results demonstrate that RRSGAN is robust and outperforms the state-of-the-art SISR and RefSR methods in both quantitative evaluation and visual results, which indicates the great potential of the RefSR method for remote sensing tasks. Our code and data are available at https://github.com/dongrunmin/RRSGAN.