Convolutional neural networks have been adopted in super-resolution algorithm inrecent years. These supervised learning methods can train the model through external image sets to improve the image resolution of specific feature such as faces or buildings. Because they need large amount of training images, it usually consumes lots of computation cost. This study implements the Zeroshot Super-resolution (ZSSR) method developed by Assaf Shocher and applied to improve the spatial resolution of Sentinel-2 images. ZSSR is a self-supervised learning method that does not need the pre-training process with image data set. It only needs to learn the internal structural features of the test image itself. The experimental results show that ZSSR can improve peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 4.67% and 3.35% compared with bicubic interpolation, which are comparable to supervised learning methods that consume training resources. The results also show, that the repeated internal structural features in remote sensing images are suitable for self-supervised learning super-resolution algorithms.
|Translated title of the contribution||Self-Supervised Super-Resolution on Sentinel-2 Imagery|
|Original language||Chinese (Traditional)|
|Number of pages||6|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|State||Published - May 2022|