论文标题
冠状孔分割，匹配和地图分类的图像处理方法
Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification
论文作者
论文摘要
本文提出了多年努力的结果，以开发和验证图像处理方法，以根据太阳图像观察选择最佳的物理模型。该方法包括根据与从图像中提取的冠状孔的一致选择物理模型。最终，目标是使用物理模型来预测地磁风暴。我们将问题分解为三个子问题：（i）基于物理约束的冠状孔分割，（ii）匹配不同地图之间的冠状孔的簇簇，以及（iii）物理地图分类。对于分割冠状孔，我们开发了一种多模式方法，该方法使用从三种不同方法的分割图来初始化一种水平集方法，该方法将初始冠状孔分割发展为磁性边界。然后，我们引入了一种基于线性编程的新方法，用于匹配冠状孔的簇。然后使用随机森林进行最终匹配。使用从多个阅读器，手动聚类，手动图分类和50个地图验证的方法验证的共识图仔细验证了这些方法。提出的多模式分割方法通过提供准确的边界检测来显着优于塞格内特，U-NET，Henney-Harvey和FCN。总体而言，该方法具有95.5％的地图分类精度。
The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introduce a new method based on Linear Programming for matching clusters of coronal holes. The final matching is then performed using Random Forests. The methods were carefully validated using consensus maps derived from multiple readers, manual clustering, manual map classification, and method validation for 50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN by providing accurate boundary detection. Overall, the method gave a 95.5% map classification accuracy.
