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Due to the costly nature of remote sensing image labeling and the largevolume of available unlabeled imagery, self-supervised methods that can learnfeature representations without manual annotation have received greatattention. While prior works have explored self-supervised learning in remotesensing tasks, pretext tasks based on local-global view alignment remainunderexplored. Inspired by DINO, which employs an effective representationlearning structure with knowledge distillation based on global-local viewalignment, we formulate two pretext tasks for use in self-supervised learningon remote sensing imagery (SSLRS). Using these tasks, we explore theeffectiveness of positive temporal contrast as well as multi-sized views onSSLRS. Moreover, we extend DINO and propose DINO-MC which uses local views ofvarious sized crops instead of a single fixed size. Our experiments demonstratethat even when pre-trained on only 10% of the dataset, DINO-MC performs on paror better than existing state of the art SSLRS methods on multiple remotesensing tasks, while using less computational resources. All codes, models andresults are available at https://github.com/WennyXY/DINO-MC.

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