An Automatic Detection of Solar Active Regions Based on Scale-Invariant Feature Transform and Clustering by Fast Search and Find of Density Peaks

The solar active regions are the sites of various activities taking place in the solar atmosphere. Accurate detection and identification of the solar active regions are of great scientific significance to understand the formation mechanism of the solar magnetic field. In this paper, we propose an automatic detection and recognition method for solar active regions based on the advantages of Scale-Invariant Feature Transform (SIFT) and Clustering by Fast Search and Find of Density Peaks (DPC). Firstly, contrast enhancement is used in the longitudinal magnetic image of Helioseismic and Magnetic Imager (HMI) of Solar Dynamics Observatory (SDO). Then, the feature points are extracted by SIFT. Finally, the feature points are clustered by fast search and find of density peaks so as to automatically detect and identify the solar active regions. The results show that the combination of SIFT and DPC can accurately identify the solar active regions without human-computer interaction.