Development of an Advanced Automated Method for Solar Filament Recognition and Its Scientific Application to a Solar Cycle of MLSO H\alpha\ Data
Qi Hao, Cheng Fang, P. F. Chen

TL;DR
This paper presents an automated method for detecting and analyzing solar filaments in H-alpha images, enabling detailed study of their evolution and migration patterns over a solar cycle.
Contribution
The authors developed a robust, efficient automated algorithm for filament recognition and tracking, applied to MLSO data spanning 1998-2009, revealing new insights into filament migration during Solar Cycle 23.
Findings
Filament migration shows three distinct phases in Solar Cycle 23.
Approximately 60% of high-latitude filaments migrate poleward at high speeds.
Northern and southern hemisphere migration speeds are similar during the cycle.
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…
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