中国科学院云南天文台机构知识库

使用快速鲁棒性主成分分析（Fast Robust Principal Component Analysis, Fast RPCA）方法对日冕序列图像中的日冕喷流活动进行检测。检测的基本思路是利用快速鲁棒性主成分分析方法中低秩和稀疏分解的思想与日冕序列图像中有着变化尺度稍小且占比较大的随机变化背景成分、变化尺度较大且占比较小的日冕喷流的特点相结合,实现随机复杂多变的动态背景和稀疏运动目标之间的分离,从而检出作为前景变化的日冕喷流。采用太阳动力学天文台（Solar Dynamics Observatory, SDO）卫星的大气成像仪（Atmospheric Imaging Assembly, AIA）两组不同时间段、不同波段、不同观测位置的日冕序列图像作为研究对象。研究内容主要包括日冕序列图像的预处理、日冕喷流检测、快速鲁棒性主成分分析方法与帧间差分法的检测结果对比分析。实验结果表明,与帧间差分法相比,快速鲁棒性主成分分析方法能够检出强度较弱的日冕喷流,且提高了日冕喷流检测的准确度。

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.