Change-Aware Sampling and Contrastive Learning for Satellite Images

Utkarsh MallBharath HariharanKavita Bala

Cornell University

In CVPR 2023


Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithms. Self-supervised learning instead provides a way to learn effective representations for various downstream tasks without labels. In this work, we leverage characteristics unique to satellite images to learn better self-supervised features. Specifically, we use the temporal signal to contrast images with long-term and short-term differences, and we leverage the fact that satellite images do not change frequently. Using these characteristics, we formulate a new loss contrastive loss called Change-Aware Contrastive (CACo) Loss. Further, we also present a novel method of sampling different geographical regions. We show that leveraging these properties leads to better performance on diverse downstream tasks. For example, we see a 6.5% relative improvement for semantic segmentation and an 8.5% relative improvement for change detection over the best-performing baseline with our method.


[pdf]   [supplementary pdf]

Utkarsh Mall, Bharath Hariharan and Kavita Bala. "Change-Aware Sampling and Contrastive Learning for Satellite Images". In CVPR, 2023.

 title={Change-Aware Sampling and Contrastive Learning for Satellite Images},
 author={Mall, Utkarsh and Hariharan, Bharath and Bala, Kavita},


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