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

Abstract

The solar active regions are the regions where various active phenomena occur 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 technology 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, enhance the contrast of longitudinal magnetic image of Helioseismic and Magnetic Imager (HMI) of Solar Dynamics Observatory (SDO). Then extract the feature points by SIFT. Finally, cluster the feature points 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 detect the solar active region without human-computer interaction.