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

• URL: http://arxiv.org/abs/2303.06670v2
• Date: Wed, 24 Apr 2024 03:28:44 GMT
• Title: Extending global-local view alignment for self-supervised learning with remote sensing imagery
• Authors: Xinye Wanyan, Sachith Seneviratne, Shuchang Shen, Michael Kirley,
• Abstract summary: Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data. Inspired by DINO, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS) We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size.
• Score: 1.5192294544599656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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