Summary of Self-supervision in Time For Satellite Images(s3-tss): a Novel Method Of Ssl Technique in Satellite Images, by Akansh Maurya et al.
Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images
by Akansh Maurya, Hewan Shrestha, Mohammad Munem Shahriar
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel self-supervised learning technique, S3-TSS, that leverages the natural augmentation occurring in the temporal dimension of remote sensing data. The method is designed to work with limited labeled data and satellite images with high temporal frequency. By comparing its results with state-of-the-art methods and performing various experiments, the authors show that S3-TSS can outperform baseline SeCo on four downstream datasets. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The idea behind this paper is to use the time dimension of remote sensing data as a way to augment images without creating artificial augmentation. This is different from other pretext-based algorithms that are not suitable for satellite images. The authors propose a self-supervised learning technique called S3-TSS, which can be used with limited labeled data and high temporal frequency satellite images. |
Keywords
» Artificial intelligence » Self supervised