Air transport poses significant environmental challenges, particularly the contribution of flight contrails to climate change due to their potential global warming impact. Detecting contrails from satellite images has been a long-standing challenge. Traditional computer vision techniques have limitations under varying image conditions, and machine learning approaches using typical convolutional neural networks are hindered by the scarcity of hand-labeled contrail datasets and contrail-tailored learning processes. In this paper, we introduce an innovative model based on augmented transfer learning that accurately detects contrails with minimal data. We also propose a novel loss function, SR Loss, which improves contrail line detection by transforming the image space into Hough space. Our research opens new avenues for machine learning-based contrail detection in aviation research, offering solutions to the lack of large hand-labeled datasets, and significantly enhancing contrail detection models.

通过增强迁移学习的创新模型，我们成功地使用较少数据准确地检测了飞行冰线，并通过将图像空间转化为霍夫空间的SR Loss提出了一种新的损失函数，为航空研究中基于机器学习的冰线检测开辟了新的途径，解决了大型手工标记数据集的缺失问题，并显著提升了冰线检测模型。

在霍夫空间中通过增强的迁移学习和新的SR损失函数完成飞行轨迹分割