Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.

翻译：航空运输带来了显著的环境挑战，特别是飞行尾迹因其潜在的全球变暖影响而在气候变化中扮演重要角色。传统计算机视觉技术在变化的遥感图像条件下表现不佳，而使用卷积神经网络的常规机器学习方法受限于人工标注尾迹数据集的稀缺性。为解决这些问题，我们采用少样本迁移学习引入了一种创新方法，仅需最少量的标注数据即可实现精确的尾迹分割。我们的方法利用在大型图像数据集上预训练的骨干分割模型，并通过增强的尾迹特定数据集进行微调。我们还引入了一种新型损失函数，称为SR损失，通过将图像空间变换到霍夫空间来增强尾迹线段检测。与通用图像分割损失函数相比，这一变换带来了显著的性能提升。我们的方法为有限标注数据带来的挑战提供了稳健的解决方案，并显著推进了尾迹检测模型的发展水平。