Image Processing, Machine Learning and Geometric Modelling for the 3D Representation of Solar Features

空间气象是一门新兴学科，它包括研究影响行星之间空间的太阳活动和喷发，特别是地球上和地球周围的空间。空间天气对人类的重要性与日俱增，原因有二：第一，它影响地球上的生命；第二，极端的空间天气影响我们越来越依赖的通信和电力系统。众所周知，某些太阳特征在太阳图像中被视为2D投影，与导致空间天气的太阳活动高度相关，该项目的目的是建立第一个这种特征结构的3D目录。这一目录将提供机会，为太阳活动预报制定新的指数，这是必要的，因为目前的指数并不可靠。我们将使用从空间和地面来源(即SOHO卫星和Meudon巴黎天文台)以不同波长拍摄的现有太阳图像，并将在将来有立体声空间飞行任务的数据时列入这些数据。这个项目感兴趣的太阳特征是太阳黑子、活动区和细丝，三维星表将通过从多波长(不同波长显示不同高度的特征)的太阳图像中提取多尺度二维特征，利用计算机自动识别太阳特征来创建。自动化系统将分析这些提取的太阳特征，以获得将用于创建相应3D模型的参数。3D模型将为感兴趣的特征提供物理和视觉描述，这将比基于文本的描述更完整，并且可能比感兴趣的特征的2D图像分割需要更少的计算机存储。这个项目也是基于2D的图像处理和机器学习以及基于3D的几何建模之间的真正融合，我们希望开发的技术将对其他可能受益于这种接口的应用程序有用。

Space weather is an emerging subject that includes studies of solar activities and eruptions that affect the space between the planets and in particular on and around the Earth. Space weather is of growing significance to mankind for two reasons: firstly it affects life on Earth and secondly extremes of space weather affect communications and power systems, on which we are becoming more and more reliant. It is well known that certain solar features, seen as 2D projections in images of the sun, are highly correlated with the solar activities responsible for space weather and the purpose of this project is to construct the first 3D catalogue of the structures of such features. Such a catalogue will provide the opportunity to develop new indexes for the forecast of solar activities, needed since the present indexes are not reliable. We will use existing solar images captured at different wavelengths from both space and ground sources (i.e. the SOHO satellite and the Meudon Paris observatory) and we will include future data from the STEREO space mission when it becomes available. The solar features of interest in this project are sunspots, active regions and filaments and the 3D catalogue will be created using automated computer recognition of solar features based on multi-scale 2D feature extraction from solar images at multiple wavelengths (different wavelengths show the shapes of features at different heights). The automated system will analyse these extracted solar features to obtain parameters that will be used to create corresponding 3D models. The 3D models will provide physical and visual descriptions for the features of interest, which will be more complete than text-based descriptions and could require less computer storage than 2D image segmentations of the features of interest. This project is also a real convergence between 2D-based image processing and machine learning and 3D-based geometric modelling and we hope that the technology developed will be useful for other applications that may benefit from such interface.