  Supervised deep neural networks are the-state-of-the-art for many tasks in
the remote sensing domain, against the fact that such techniques require the
dataset consisting of pairs of input and label, which are rare and expensive to
collect in term of both manpower and resources. On the other hand, there are
abundance of raw satellite images available both for commercial and academic
purposes. Hence, in this work, we tackle the insufficient labeled data problem
in satellite image classification task by introducing the process based on the
self-supervised learning technique to create the synthetic labels for satellite
image patches. These synthetic labels can be used as the training dataset for
the existing supervised learning techniques. In our experiments, we show that
the models trained on the synthetic labels give similar performance to the
models trained on the real labels. And in the process of creating the synthetic
labels, we also obtain the visual representation vectors that are versatile and
knowledge transferable.
