  In recent years self-supervised learning has emerged as a promising candidate
for unsupervised representation learning. In the visual domain its applications
are mostly studied in the context of images of natural scenes. However, its
applicability is especially interesting in specific areas, like remote sensing
and medicine, where it is hard to obtain huge amounts of labeled data. In this
work, we conduct an extensive analysis of the applicability of self-supervised
learning in remote sensing image classification. We analyze the influence of
the number and domain of images used for self-supervised pre-training on the
performance on downstream tasks. We show that, for the downstream task of
remote sensing image classification, using self-supervised pre-training on
remote sensing images can give better results than using supervised
pre-training on images of natural scenes. Besides, we also show that
self-supervised pre-training can be easily extended to multispectral images
producing even better results on our downstream tasks.
