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Air transport poses significant environmental challenges, particularlyregarding the role of flight contrails in climate change due to their potentialglobal warming impact. Traditional computer vision techniques struggle undervarying remote sensing image conditions, and conventional machine learningapproaches using convolutional neural networks are limited by the scarcity ofhand-labeled contrail datasets. To address these issues, we employ few-shottransfer learning to introduce an innovative approach for accurate contrailsegmentation with minimal labeled data. Our methodology leverages backbonesegmentation models pre-trained on extensive image datasets and fine-tunedusing an augmented contrail-specific dataset. We also introduce a novel lossfunction, termed SR Loss, which enhances contrail line detection bytransforming the image space into Hough space. This transformation results in asignificant performance improvement over generic image segmentation lossfunctions. Our approach offers a robust solution to the challenges posed bylimited labeled data and significantly advances the state of contrail detectionmodels.

Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space