科研之友
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摘要
The increase in the amount of solar data provided by new satellites makes it necessary to develop methods to automate the detection of solar features. Here we present a method for automatically detecting active regions in solar extreme ultraviolet (EUV) images using a series of steps. Initially, the bright regions in the image are segmented using seeded region growing. In a second phase these bright regions are clustered into active regions. Partition-based clustering (both hard and fuzzy) and hierarchical clustering are compared in this work. The aim of the clustering phase is to associate a group to each segmented region in order to reduce the total number of active regions. This facilitates the documentation or subsequent monitoring of these regions. We use two indicators to validate the partitioning: i) the number of detected clusters approximates the number of active regions reported by the National Oceanic and Atmospheric Administration (NOAA) and ii) the area that defines each cluster overlaps with the area of an active region of NOAA. Experiments have been performed on over 6000 images from SOHO/EIT (195 ). The best results were obtained using hierarchical clustering. The method detects a set of active regions in an image of the solar corona that successfully matches the number of NOAA regions. We will use these regions to perform real-time monitoring and flare detection.
- 出版日期2013-4
