  The capabilities of super-resolution reconstruction (SRR)---techniques for
enhancing image spatial resolution---have been recently improved significantly
by the use of deep convolutional neural networks. Commonly, such networks are
learned using huge training sets composed of original images alongside their
low-resolution counterparts, obtained with bicubic downsampling. In this paper,
we investigate how the SRR performance is influenced by the way such
low-resolution training data are obtained, which has not been explored up to
date. Our extensive experimental study indicates that the training data
characteristics have a large impact on the reconstruction accuracy, and the
widely-adopted approach is not the most effective for dealing with satellite
images. Overall, we argue that developing better training data preparation
routines may be pivotal in making SRR suitable for real-world applications.
