  Super-resolution, which aims to reconstruct high-resolution images from
low-resolution images, has drawn considerable attention and has been
intensively studied in computer vision and remote sensing communities. The
super-resolution technology is especially beneficial for Unmanned Aerial
Vehicles (UAV), as the amount and resolution of images captured by UAV 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 paper, 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 will be available at
https://github.com/lironui/LSwinSR.
