  Since large number of high-quality remote sensing images are readily
accessible, exploiting the corpus of images with less manual annotation draws
increasing attention. Self-supervised models acquire general feature
representations by formulating a pretext task that generates pseudo-labels for
massive unlabeled data to provide supervision for training. While prior studies
have explored multiple self-supervised learning techniques in remote sensing
domain, pretext tasks based on local-global view alignment remain
underexplored, despite achieving state-of-the-art results on natural imagery.
Inspired by DINO, which employs an effective representation learning structure
with knowledge distillation based on global-local view alignment, we formulate
two pretext tasks for self-supervised learning on remote sensing imagery
(SSLRS). Using these tasks, we explore the effectiveness of positive temporal
contrast as well as multi-sized views on SSLRS. We extend DINO and propose
DINO-MC which uses local views of various sized crops instead of a single fixed
size in order to alleviate the limited variation in object size observed in
remote sensing imagery. Our experiments demonstrate that even when pre-trained
on only 10% of the dataset, DINO-MC performs on par or better than existing
state-of-the-art SSLRS methods on multiple remote sensing tasks, while using
less computational resources. All codes, models, and results are released at
https://github.com/WennyXY/DINO-MC.
