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Since large number of high-quality remote sensing images are readilyaccessible, exploiting the corpus of images with less manual annotation drawsincreasing attention. Self-supervised models acquire general featurerepresentations by formulating a pretext task that generates pseudo-labels formassive unlabeled data to provide supervision for training. While prior studieshave explored multiple self-supervised learning techniques in remote sensingdomain, pretext tasks based on local-global view alignment remainunderexplored, despite achieving state-of-the-art results on natural imagery.Inspired by DINO, which employs an effective representation learning structurewith knowledge distillation based on global-local view alignment, we formulatetwo pretext tasks for self-supervised learning on remote sensing imagery(SSLRS). Using these tasks, we explore the effectiveness of positive temporalcontrast as well as multi-sized views on SSLRS. We extend DINO and proposeDINO-MC which uses local views of various sized crops instead of a single fixedsize in order to alleviate the limited variation in object size observed inremote sensing imagery. Our experiments demonstrate that even when pre-trainedon only 10% of the dataset, DINO-MC performs on par or better than existingstate-of-the-art SSLRS methods on multiple remote sensing tasks, while usingless computational resources. All codes, models, and results are released athttps://github.com/WennyXY/DINO-MC.

Extending global-local view alignment for self-supervised learning with remote sensing imagery