An Advanced Automated Method for Solar Filament Recognition

Qi Hao, Cheng Fang, Peng-Fei Chen

Abstract:

We developed a method to automatically detect and trace solar filaments in
H$\alpha$ full-disk images. The program is able not only to recognize
filaments and determine their properties, such as the position, the area, the
spine, and other relevant parameters, but also to trace the daily evolution of
the filaments. The program consists of three steps: First, preprocessing is
applied to correct the original images; Second, the Canny edge-detection method
is used to detect filaments; Third, filament properties are recognized
through the morphological operators. To test the algorithm, we applied it to
the observations from the Mauna Loa Solar Observatory (MLSO), and the program is
demonstrated to be robust and efficient. H$\alpha$ images obtained by MLSO from
1998 to 2009 are analyzed, and a butterfly diagram of filaments is
obtained. It shows that the latitudinal migration of solar filaments has three
trends in the Solar Cycle 23: The drift velocity was fast from 1998 to the
solar maximum; After the solar maximum, it became relatively slow. After 2006,
the migration became divergent, signifying the solar minimum. About 60\%
filaments with the latitudes larger than $50^{\circ}$ migrate towards the polar
regions with relatively high velocities, and the latitudinal migrating speeds
in the northern and the southern hemispheres do not differ significantly in the
Solar Cycle 23.