The morphology of the auroral oval is an important geophysical parameter that helps to further understand the solar wind‐magnetosphere‐ionosphere coupling process. However, it is still a challenging task to automatically obtain auroral poleward and equatorward boundaries completely and accurately. In this paper, a new model based on the deep feature and dual level set method is proposed to extract the auroral oval boundaries in the images acquired by the Ultraviolet Imager (UVI) onboard the Polar spacecraft. With the deep feature extracted by the convolutional neural network (CNN), the corresponding deep feature energy functional is constructed and incorporated into the variational segmentation framework. The dual level set method is implemented to extract the accurate poleward and equatorward boundaries with the gradient descent flow. The experimental results on the test data set demonstrate that this model can extract complete auroral oval contours that are consistent well with annotations and owns higher accuracy compared with the previously proposed methods. Comparison between the extracted auroral boundaries and the precipitating boundaries determined by Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle data validates that the proposed method is trustworthy to capture the global morphology of the auroral ovals.

中文翻译：

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具有深度特征和双水平集的自动极光边界确定算法

极光椭圆的形态是一个重要的地球物理参数，有助于进一步了解太阳风-磁层-电离层耦合过程。但是，自动，完全，准确地获得极光的极地和赤道边界仍然是一项艰巨的任务。本文提出了一种基于深度特征和双水平集方法的新模型，以提取极地飞船上的紫外线成像仪（UVI）采集的图像中的极光椭圆边界。利用卷积神经网络（CNN）提取的深层特征，构造了相应的深层特征能量泛函，并将其整合到变分分割框架中。实施双水平集方法以提取具有梯度下降流的精确的极向和赤道边界。测试数据集上的实验结果表明，与先前提出的方法相比，该模型可以提取出完全符合注释的极光椭圆形轮廓，并且具有更高的准确性。提取的极光边界和由国防气象卫星计划（DMSP）SSJ沉淀粒子数据确定的沉淀边界之间的比较验证了所提出的方法对于捕获极光椭圆的整体形态是值得信赖的。