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Li Weijiang, Qi Xin, Wang Feng. Recognition of Solar Activities Based on Heliophysics Event Knowledgebase[J]. Astronomical Research and Technology, 2017, 14(2): 251-260.
Citation: Li Weijiang, Qi Xin, Wang Feng. Recognition of Solar Activities Based on Heliophysics Event Knowledgebase[J]. Astronomical Research and Technology, 2017, 14(2): 251-260.

Recognition of Solar Activities Based on Heliophysics Event Knowledgebase

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  • Received Date: July 03, 2016
  • Revised Date: July 27, 2016
  • Available Online: November 20, 2023
  • Solar image conveys obvious active regions and peaceful regions of no activity. Recognizing useful solar activities from solar image is a typical application of image processing in the astronomical research scope. Benefiting from real-time observation data provided by Heliophysics Event Knowledgebase (HEK) of Solar Dynamics Observatory (SDO), this paper proposes a recognition method of solar activities based on HEK. In this method, we gather six kinds of solar activities datas (date,location,area), then we design a scaling transform model for full-disk solar image of corresponding date. Combining location data and area data, we design different gradient thresholds to segment different solar activities regions. Two kinds of boundary identification method are used to locate and recognize solar activities regions respectively. Furthermore, we study the correlation of six kinds of solar image features, and give combinations of features for every solar activity. The method in the paper realizes the precise positioning and efficient recognition, which lies the foundation for further work. Besides, extracting particular combination of features from different solar activities regions also provides a practical approach of constructing a condensed feature set for CBIR system.
  • [1]
    Pesnell W D, Thompson B J, Chamberlin P C. The Solar Dynamics Observatory (SDO)[J]. Solar Physics, 2012, 275(1-2): 3-15.
    [2]
    Kempton D, Pillai K G, Angryk R. Iterative refinement of multiple targets tracking of solar events[C]//2nd IEEE International Conference on Big Data. 2014.
    [3]
    Banda J M, Liu C, Angryk R A. Region-based querying of solar data using descriptor signatures[C]//IEEE Conference on Data Mining Workshops. 2013.
    [4]
    Zharkova V V, Ipson S S, Benkhalil A, et al. Feature recognition in solar images[J]. Artificial Intelligence Review, 2005, 23(3): 209-266.
    [5]
    Benkhalil A, Zharkova V V, Zharkov S, et al. Automatic identification of Active Regions (Plages) in the full-disk solar images using local thresholding and region growing techniques[C]//Proceedings of the AISB'03 Symposium on Biologically-inspired Machine Vision, Theory and Application. 2003.
    [6]
    Zharkova V V, Schetinin V. Filament recognition in solar images with the neural network technique[J]. Solar Physics. 2005, 228(1-2): 137-148.
    [7]
    Bernasconi P N, Rust D M, Hakim D. Advanced automated solar filament detection and characterization code: description, performance, and results[J]. Solar Physics, 2005, 228(1): 97-117.
    [8]
    Christe S, Hannah I G, Krucker S, et al. RHESSI microflare statistics. I. flare-finding and frequency distributions[J]. The Astrophysical Journal, 2008, 677(2): 1385-1394.
    [9]
    Savcheva A, Cirtain J, Deluca E E, et al. A study of polar jet parameters based on Hinode XRT observations[J]. Publications of the Astronomical Society of Japan, 2007, 59(SP3): S771-S778.
    [10]
    Hurlburt N, Cheung M, Schrijver C, et al. Heliophysics Event Knowledgebase for the Solar Dynamics Observatory (SDO) and beyond[J]. Solar Physics, 2012, 275(1-2): 67-78.
    [11]
    李卫疆, 亓鑫, 王锋. 基于方形网格结构的太阳活动目标检测方法[J]. 天文研究与技术, 2016, 13(4): 481-488.

    Li Weijiang, Qi Xin, Wang Feng. A square grid structure for target detection of solar activities[J]. Astronomical Research & Technology, 2016, 13(4): 481-488.
    [12]
    卢蓉, 范勇, 陈念年, 等. 一种提取目标图像最小外接矩形的快速算法[J]. 计算机工程, 2010, 36(21): 178-180.

    Lu Rong, Fan Yong, Chen Niannian, et al. Fast algorithm for extraction Minimum Enclosing Rectangle of target image[J]. Computer Engineering, 2010, 36(21): 178-180.
    [13]
    Vasconcelos N, Vasconcelos M. Scalable discriminant feature selection for image retrieval and recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004.
    [14]
    Deselaers T, Keysers D, Ney H. Features for image retrieval: an experimental comparison[J]. Information Retrieval, 2008, 11(2): 77-107.
    [15]
    Martens P C H, Attrill G D R, Davey A R, et al. Computer vision for the Solar Dynamics Observatory (SDO)[J]. Solar Physics, 2012, 275(1-2): 79-113.

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