  With the development of deep learning, supervised learning methods perform
well in remote sensing images (RSIs) scene classification. However, supervised
learning requires a huge number of annotated data for training. When labeled
samples are not sufficient, the most common solution is to fine-tune the
pre-training models using a large natural image dataset (e.g. ImageNet).
However, this learning paradigm is not a panacea, especially when the target
remote sensing images (e.g. multispectral and hyperspectral data) have
different imaging mechanisms from RGB natural images. To solve this problem, we
introduce new self-supervised learning (SSL) mechanism to obtain the
high-performance pre-training model for RSIs scene classification from large
unlabeled data. Experiments on three commonly used RSIs scene classification
datasets demonstrated that this new learning paradigm outperforms the
traditional dominant ImageNet pre-trained model. Moreover, we analyze the
impacts of several factors in SSL on RSIs scene classification tasks, including
the choice of self-supervised signals, the domain difference between the source
and target dataset, and the amount of pre-training data. The insights distilled
from our studies can help to foster the development of SSL in the remote
sensing community. Since SSL could learn from unlabeled massive RSIs which are
extremely easy to obtain, it will be a potentially promising way to alleviate
dependence on labeled samples and thus efficiently solve many problems, such as
global mapping.
