Abstract
极光卵形态提取是极光研究的重要手段.如何提高强干扰背景下的紫外极光图像极光卵形态提取精度，目前仍是一个难题.本文提出一种基于深度学习语义分割模型U-net的方法，实现了对极光卵形态的高精度提取.在Polar卫星紫外极光观测数据的实验结果表明，该方法相比于已有算法精度更高，对完整型极光卵和缺口型极光卵图像均能得到更加精确的提取结果，特别是针对强日辉干扰、灰度不均匀和对比度低情况下的紫外极光图像时，该方法显示了明显优势.
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