LSwinSR: UAV Imagery Super-Resolution Based on Linear Swin Transformer

Abstract

Super-resolution, which aims to reconstruct high-resolution (HR) images from low-resolution (LR) images, has drawn considerable attention and has been intensively studied in computer vision and remote sensing communities. Super-resolution technology is especially beneficial for unmanned aerial vehicles (UAVs), as the number and resolution of images captured by UAVs are highly limited by physical constraints such as flight altitude and load capacity. In the wake of the successful application of deep learning methods in the super-resolution task, in recent years, a series of super-resolution algorithms have been developed. In this article, for the super-resolution of UAV images, a novel network based on the state-of-the-art Swin Transformer is proposed with better efficiency and competitive accuracy. Meanwhile, as one of the essential applications of the UAV is land cover and land use monitoring, simple image quality assessments such as the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) are not enough to comprehensively measure the performance of an algorithm. Therefore, we further investigate the effectiveness of super-resolution methods using the accuracy of semantic segmentation. The code is available at https://github.com/lironui/GeoSR .