J. Space Weather Space Clim.
Volume 12, 2022
|Number of page(s)||22|
|Published online||21 June 2022|
Multi-scale image preprocessing and feature tracking for remote CME characterization
Institute of Astronomy and National Astronomical Observatory, Bulgarian Academy of Sciences, Tsarigradsko Chausee Blvd 72, Sofia 1784, Bulgaria
* Corresponding author: firstname.lastname@example.org
Accepted: 24 May 2022
Coronal Mass Ejections (CMEs) influence the interplanetary environment over vast distances in the solar system by injecting huge clouds of fast solar plasma and energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced, but current understanding points to CME-driven shocks and compressions in the solar corona. At the same time, unprecedented remote and in situ (Parker Solar Probe, Solar Orbiter) solar observations are becoming available to constrain existing theories. Here we present a general method for recognition and tracking solar images of objects such as CME shock waves and filaments. The calculation scheme is based on a multi-scale data representation concept à trous wavelet transform, and a set of image filtering techniques. We showcase its performance on a small set of CME-related phenomena observed with the SDO/AIA telescope. With the data represented hierarchically on different decomposition and intensity levels, our method allows extracting certain objects and their masks from the imaging observations in order to track their evolution in time. The method presented here is general and applicable to detecting and tracking various solar and heliospheric phenomena in imaging observations. It holds the potential to prepare large training data sets for deep learning. We have implemented this method into a freely available Python library.
Key words: Coronal bright fronts / coronal mass ejections / image processing / eruptive filaments / CME
© O. Stepanyuk et al., Published by EDP Sciences 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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