Coronal Jet Automatic Detection Method Based on Fast Robust Principal Component Analysis

In this paper, fast robust principal component analysis (Fast RPCA) is used to detect coronal jet activity in coronal sequence images. The basic idea of detection is to combine the idea of low rank and sparse decomposition in Fast RPCA method with the characteristics of coronal sequence images, such as the random background component with smaller scale and larger proportion, and the coronal jet with larger scale and smaller proportion, so as to realize the separation between the random and complex dynamic background and sparse moving objects, as well as to detect the coronal jet as the foreground change. Two sets of coronal sequence images of different time periods, different channel and different observation positions from the atmospheric imaging assembly (AIA) observation equipment on the solar dynamics observatory (SDO) satellite are used as the research objects. The main research contents include the preprocessing of coronal image sequence, coronal jet detection, and the comparison analysis of the detection results between Fast RPCA method and running difference method. The experimental results show that compared with the running difference method, the Fast RPCA method can detect the weak coronal jet and improve the accuracy of coronal jet detection.