DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops

12 Mar 2023  ·  Xinye Wanyan, Sachith Seneviratne, Shuchang Shen, Michael Kirley ·

Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention. While prior works have explored self-supervised learning in remote sensing tasks, pretext tasks based on local-global view alignment remain underexplored. 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 use in 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. Moreover, we extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size. 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 available at https://github.com/WennyXY/DINO-MC.

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Results from the Paper

 Ranked #1 on Multi-Label Image Classification on BigEarthNet-10% (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Multi-Label Image Classification BigEarthNet DINO-MC mAP (micro) 88.75 # 5
official split No # 1
Multi-Label Image Classification BigEarthNet-10% DINO-MC mean average precision 84.20 # 1
Image Classification EuroSAT DINO-MC (WRN linear eval)) Accuracy (%) 95.7 # 9
Image Classification EuroSAT DINO-MC (Wide ResNet) Accuracy (%) 98.78 # 5
Change Detection OSCD - 13ch DINO-MC (WRN-50) Precision 49.99 # 3
F1 52.7 # 3