Change-Aware Sampling and Contrastive Learning for Satellite Images
Utkarsh Mall, Bharath Hariharan, Kavita Bala
Cornell University
In CVPR 2023
Abstract
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.
Video
An 8-minute presentation of our work.
Paper
[pdf] [supplementary pdf]
Utkarsh Mall, Bharath Hariharan and Kavita Bala. "Change-Aware Sampling and Contrastive Learning for Satellite Images". In CVPR, 2023.
@inproceedings{caco-23,
title={Change-Aware Sampling and Contrastive Learning for Satellite Images},
author={Mall, Utkarsh and Hariharan, Bharath and Bala, Kavita},
booktitle={CVPR},
year={2023}
}
Poster
