  Currently, when reliable training datasets are available, deep learning
methods dominate the proposed solutions for image super-resolution. However,
for remote sensing benchmarks, it is very expensive to obtain high spatial
resolution images. Most of the super-resolution methods use down-sampling
techniques to simulate low and high spatial resolution pairs and construct the
training samples. To solve this issue, the paper introduces a novel public
remote sensing dataset (Alsat2B) of low and high spatial resolution images (10m
and 2.5m respectively) for the single-image super-resolution task. The
high-resolution images are obtained through pan-sharpening. Besides, the
performance of some super-resolution methods on the dataset is assessed based
on common criteria. The obtained results reveal that the proposed scheme is
promising and highlight the challenges in the dataset which shows the need for
advanced methods to grasp the relationship between the low and high-resolution
patches.
