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Automated Solar Feature Detection for Space Weather Applications
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Author(s): David Pérez-Suárez (Trinity College Dublin, Ireland), Paul A. Higgins (Trinity College Dublin, Ireland), D. Shaun Bloomfield (Trinity College Dublin, Ireland), R.T. James McAteer (Trinity College Dublin, Ireland), Larisza D. Krista (Trinity College Dublin, Ireland), Jason P. Byrne (Trinity College Dublin, Ireland)and Peter. T. Gallagher (Trinity College Dublin, Ireland)
Copyright: 2013
Pages: 19
Source title:
Image Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3994-2.ch049
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Abstract
The solar surface and atmosphere are highly dynamic plasma environments, which evolve over a wide range of temporal and spatial scales. Large-scale eruptions, such as coronal mass ejections, can be accelerated to millions of kilometers per hour in a matter of minutes, making their automated detection and characterisation challenging. Additionally, there are numerous faint solar features, such as coronal holes and coronal dimmings, which are important for space weather monitoring and forecasting, but their low intensity and sometimes transient nature makes them problematic to detect using traditional image processing techniques. These difficulties are compounded by advances in ground- and space- based instrumentation, which have increased the volume of data that solar physicists are confronted with on a minute-by-minute basis; NASA’s Solar Dynamics Observatory for example is returning many thousands of images per hour (~1.5 TB/day). This chapter reviews recent advances in the application of images processing techniques to the automated detection of active regions, coronal holes, filaments, CMEs, and coronal dimmings for the purposes of space weather monitoring and prediction.
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