Towards Unsupervised Multi-Temporal Satellite Image Super-Resolution

Multi-temporal super-resolution (SR) whereby a number of images of the same scene acquired at different times are fused to enhance its spatial resolution has recently enjoyed great success thanks to advances in deep learning methods. However, the literature has so far focused on supervised training approaches that require the availability of high-resolution (HR) images at the target resolution. This is a significant limitation because such imagery may not exist, might be difficult to source or exhibit domain gaps such as different spectral bands or radiometric characteristics. Unsupervised training approaches that do not require imagery beyond the input low resolution are needed to overcome this limitation. This paper presents a first analysis of the problem, taking inspiration from the literature on blind single-image SR, but also focusing on the uniqueness of multi-temporal satellite images. Our preliminary results show that it is indeed possible to develop accurate deep learning models for multi-temporal SR without HR images.