论文标题
自我监督的遥感功能学习：学习范式，挑战和未来的作品
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
论文作者
论文摘要
深度学习在从巨大的遥感图像（RSIS）中学习功能方面取得了巨大的成功。为了更好地了解特征学习范例（例如，无监督的特征学习（USFL），监督功能学习（SFL）和自我监督的特征学习（SSFL）（SSFL）），本文分析并从特征学习信号的角度进行比较，并提供一个统一的功能学习框架。在这个统一的框架下，我们分析了SSFL在RSIS理解任务中的其他两个学习范式中的优势，并对现有的SSFL工作进行了RS的全面审查，包括预培训数据集，自我监督的功能学习信号以及评估方法。我们进一步分析了SSFL信号和预训练数据对学习功能的影响，以提供改善RSI功能学习的见解。最后，我们简要讨论一些开放问题和可能的研究方向。
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g., unsupervised feature learning (USFL), supervised feature learning (SFL), and self-supervised feature learning (SSFL)), this paper analyzes and compares them from the perspective of feature learning signals, and gives a unified feature learning framework. Under this unified framework, we analyze the advantages of SSFL over the other two learning paradigms in RSIs understanding tasks and give a comprehensive review of the existing SSFL work in RS, including the pre-training dataset, self-supervised feature learning signals, and the evaluation methods. We further analyze the effect of SSFL signals and pre-training data on the learned features to provide insights for improving the RSI feature learning. Finally, we briefly discuss some open problems and possible research directions.
