Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images
This work addresses the challenge of data scarcity in remote sensing for researchers and practitioners, though it appears incremental as it builds on existing self-supervised techniques.
The authors tackled the problem of limited labeled data in satellite imagery by proposing S3-TSS, a self-supervised learning method that uses temporal augmentation, and it outperformed the baseline SeCo on four downstream datasets.
With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms. Few pretext-based algorithms, including from rotation, spatial context and jigsaw puzzles are not appropriate for satellite images. Often, satellite images have a higher temporal frequency. So, the temporal dimension of remote sensing data provides natural augmentation without requiring us to create artificial augmentation of images. Here, we propose S3-TSS, a novel method of self-supervised learning technique that leverages natural augmentation occurring in temporal dimension. We compare our results with current state-of-the-art methods and also perform various experiments. We observed that our method was able to perform better than baseline SeCo in four downstream datasets. Code for our work can be found here: https://github.com/hewanshrestha/Why-Self-Supervision-in-Time