A CME Automatic Detection Method Based on Adaptive Background Learning Technology
In this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME dete...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Astronomy |
| Online Access: | http://dx.doi.org/10.1155/2019/6582104 |
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| author | Zhenping Qiang Xianyong Bai Qinghui Zhang Hong Lin |
| author_facet | Zhenping Qiang Xianyong Bai Qinghui Zhang Hong Lin |
| author_sort | Zhenping Qiang |
| collection | DOAJ |
| description | In this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME detection module. The core of the method is based on adaptive background learning, where CMEs are assumed to be a foreground moving object outward as observed in running-difference time series. Using the static and dynamic features to model the corona observation scene can more accurately describe the complex background. Moreover, the method can detect the subtle changes in the corona sequences while filtering their noise effectively. We applied this method to a month of continuous corona images, compared the result with CDAW, CACTus, SEEDS, and CORIMP catalogs and found a good detection rate in the automatic methods. It detected about 73% of the CMEs listed in the CDAW CME catalog, which is identified by human visual inspection. Currently, the derived parameters are position angle, angular width, linear velocity, minimum velocity, and maximum velocity of CMES. Other parameters could also easily be added if needed. |
| format | Article |
| id | doaj-art-daf34fd103fd405eab05ab4d0fca04c9 |
| institution | Kabale University |
| issn | 1687-7969 1687-7977 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Astronomy |
| spelling | doaj-art-daf34fd103fd405eab05ab4d0fca04c92025-02-03T05:54:28ZengWileyAdvances in Astronomy1687-79691687-79772019-01-01201910.1155/2019/65821046582104A CME Automatic Detection Method Based on Adaptive Background Learning TechnologyZhenping Qiang0Xianyong Bai1Qinghui Zhang2Hong Lin3College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCAS Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaIn this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME detection module. The core of the method is based on adaptive background learning, where CMEs are assumed to be a foreground moving object outward as observed in running-difference time series. Using the static and dynamic features to model the corona observation scene can more accurately describe the complex background. Moreover, the method can detect the subtle changes in the corona sequences while filtering their noise effectively. We applied this method to a month of continuous corona images, compared the result with CDAW, CACTus, SEEDS, and CORIMP catalogs and found a good detection rate in the automatic methods. It detected about 73% of the CMEs listed in the CDAW CME catalog, which is identified by human visual inspection. Currently, the derived parameters are position angle, angular width, linear velocity, minimum velocity, and maximum velocity of CMES. Other parameters could also easily be added if needed.http://dx.doi.org/10.1155/2019/6582104 |
| spellingShingle | Zhenping Qiang Xianyong Bai Qinghui Zhang Hong Lin A CME Automatic Detection Method Based on Adaptive Background Learning Technology Advances in Astronomy |
| title | A CME Automatic Detection Method Based on Adaptive Background Learning Technology |
| title_full | A CME Automatic Detection Method Based on Adaptive Background Learning Technology |
| title_fullStr | A CME Automatic Detection Method Based on Adaptive Background Learning Technology |
| title_full_unstemmed | A CME Automatic Detection Method Based on Adaptive Background Learning Technology |
| title_short | A CME Automatic Detection Method Based on Adaptive Background Learning Technology |
| title_sort | cme automatic detection method based on adaptive background learning technology |
| url | http://dx.doi.org/10.1155/2019/6582104 |
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