  High-resolution images for remote sensing applications are often not
affordable or accessible, especially when in need of a wide temporal span of
recordings. Given the easy access to low-resolution (LR) images from
satellites, many remote sensing works rely on this type of data. The problem is
that LR images are not appropriate for semantic segmentation, due to the need
for high-quality data for accurate pixel prediction for this task. In this
paper, we propose an end-to-end framework that unites a super-resolution and a
semantic segmentation module in order to produce accurate thematic maps from LR
inputs. It allows the semantic segmentation network to conduct the
reconstruction process, modifying the input image with helpful textures. We
evaluate the framework with three remote sensing datasets. The results show
that the framework is capable of achieving a semantic segmentation performance
close to native high-resolution data, while also surpassing the performance of
a network trained with LR inputs.
