Automatic Three-dimensional Reconstruction of Coronal Mass Ejection from STEREO A/B White-light Coronagraph Images

Solar activity and its effects on terrestrial and near-terrestrial environments have attained major attention in recent times. Ejections of plasma and magnetic field from the solar atmosphere are capable of disturbing interplanetary medium and producing geomagnetic storms on Earth. The main vehicles of these highly energetic events are the Coronal Mass Ejections (CMEs). One of the missions to observe these solar phenomena is a Solar Terrestrial Relations Observatory (STEREO) Ahead/Behind (A/B) mission. STEREO uses two spacecraft with almost identical instrumentation consisting of a series of coronagraphs (COR) and heliospheric imagers (HI). COR and HI images captured by STEREO A/B spacecraft allow for tracking a CME from two different viewpoints and reconstructing its propagation in a three-dimensional (3D) space. Such 3D reconstruction can be accomplished by using a technique of geometrical triangulation. The geometric triangulation technique, however, requires the location of a CME leading edge in each image to be determined first, followed by an estimation of CME propagation parameters. However, there is currently no robust, reliable, and automatic method to derive CME kinematic properties by tracking a CME leading edge continuously in COR and HI images.;To our best knowledge, this dissertation is the first systematic approach to address this issue and to develop a fully automatic pipeline for the CME leading edge tracking and estimation of propagation parameters in COR and HI images. The methods proposed in this dissertation are based on algorithms derived from data analysis with further application of image processing and pattern recognition techniques. The proposed methods include 3 individual modules: one unique approach to segment images in a stack (Pre-processing module) and two different novel approaches to track a CME leading edge and estimate propagation parameters in the stack of segmented images (Tracking modules 1 and 2). Pre-processing module allows for effective background removal and CME segmentation. The output of Pre-processing module is a set of running-difference binary images which can be fed into Tracking module 1 (or 2) to track a CME leading edge and to estimate the propagation parameters. The methods were validated using the selected CME events captured in the period from 1 January 2008 to 31 August 2009. The results demonstrate that the proposed pipeline is effective for CME leading edge tracking and CME propagation parameters estimation. Integration of these innovative approaches with the technique of geometric triangulation will provide a necessary tool for automatic estimation of 3D CME properties.