In the pursuit of environmental sustainability, the aviation industry faces
the challenge of minimizing its ecological footprint. Among the key solutions
is contrail avoidance, targeting the linear ice-crystal clouds produced by
aircraft exhaust. These contrails exacerbate global warming by trapping
atmospheric heat, necessitating precise segmentation and comprehensive analysis
of contrail images to gauge their environmental impact. However, this
segmentation task is complex due to the varying appearances of contrails under
different atmospheric conditions and potential misalignment issues in
predictive modeling. This paper presents an innovative deep-learning approach
utilizing the efficient net-b4 encoder for feature extraction, seamlessly
integrating misalignment correction, soft labeling, and pseudo-labeling
techniques to enhance the accuracy and efficiency of contrail detection in
satellite imagery. The proposed methodology aims to redefine contrail image
analysis and contribute to the objectives of sustainable aviation by providing
a robust framework for precise contrail detection and analysis in satellite
imagery, thus aiding in the mitigation of aviation's environmental impact.

这篇论文利用先进的深度学习方法，结合了高效的特征提取、校正对齐、软标签和伪标签技术，以提高对卫星图像中的对流层云追踪的准确性和效率，从而为可持续航空的目标做贡献。