Change-Aware Sampling and Contrastive Learning for Satellite Images
Change-Aware Sampling and Contrastive Learning for Satellite Images
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DOI：
10.1109/cvpr52729.2023.00509
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Utkarsh Mall;Bharath Hariharan;Kavita Bala
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文献类型：
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作者：
Utkarsh Mall;Bharath Hariharan;Kavita Bala
自动遥感工具可以帮助通知许多大规模的挑战，如灾害管理，气候变化等，虽然大量的时空卫星图像数据是现成的，其中大部分仍然没有标记。如果没有标签，这些数据对监督学习算法不是很有用。相反，自监督学习提供了一种方法，可以在没有标签的情况下学习各种下游任务的有效表示。在这项工作中，我们利用卫星图像特有的特征来学习更好的自监督特征。具体来说，我们使用时间信号来对比具有长期和短期差异的图像，并且我们利用卫星图像不经常变化的事实。利用这些特点，我们制定了一个新的损失对比损失称为变化感知对比（CACo）损失。此外，我们还提出了一种新的方法，采样不同的地理区域。我们表明，利用这些属性导致更好的性能在不同的下游任务。例如，我们看到语义分割的相对改进为6.5%，变化检测的相对改进为8.5%。
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.