Spatial-Temporal Poleward Volume Local Binary Patterns for Aurora Sequences Event Detection
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    Abstract:

    In this paper, a method for recognizing the arc aurora sequences from all-sky image sequences is proposed. For the movement trend of arc aurora sequences, a method named ST-PVLBP (spatial-temporal poleward volume local binary patterns), which is based on VLBP (volume local binary patterns) and uses ST-PVLBP to present the aurora sequences, is proposed. Combined with the interframe continuity information of the sequence and the spatial location information of the single frame, the algorithm reduces the feature dimension while maintaining high classification accuracy at the same time. The proposed method was evaluated using auroral observations at the Chinese Arctic Yellow River Station. Experimental results show that the proposed method can effectively detect the poleward moving arc aurora sequences.

    Reference
    [1] Xing ZY, Yang HG, Han DS, Wu ZS, Liu JM, Zhang QH, Liu YH, Zhang BC, Hu HQ. Multi-Instrument study of poleward moving auroral forms during different interplanetary magnetic field conditions. Chinese Journal of Polar Research, 2013,25(1):25~44 (in Chinese with English abstract). [doi: 10.3724/SP.J.1084.2013.00035]
    [2] Zhang QH, Dunlop MW, Lockwood M, Liu RY, Hu HQ, Yang HG, Hu ZJ, Bogdanova YV, Shen C, Zhang BC, Han DS, Liu SL, McCrea IW, Lester M. Simultaneous observations of reconnection pulses at cluster and their effects on the cusp aurora observed at the Chinese Yellow River Station. Journal of Geophysical Research, 2010,115:A10237. [doi: 10.1029/2010JA015526]
    [3] Hu HQ, Liu RY, Wang JF, Yang HG, Makita K, Wang X, Sato N. Statistic characteristics of the aurora observed at Zhongshan Station, Antarctica. Chinese Journal of Polar Research, 1999,11(1):8~18 (in Chinese with English abstract).
    [4] Hu ZJ, Yang H, Huang D, Araki T, Sato N, Taguchi M, Seran E, Hu H, Liu R, Zhang B, Han D, Chen Z, Zhang Q, Liang J, Liu S. Synoptic distribution of dayside aurora: Multiple-Wavelength all-sky observation at Yellow River Station in Ny-Ålesund, Svalbard. Journal of Atmospheric and Solar-Terrestrial Physics, 2009,71(8-9):794~804. [doi: 10.1016/j.jastp.2009.02.010]
    [5] Syrjäsuo MT, Donovan EF. Diurnal auroral occurrence statistics obtained via machine vision. Annales Geophysicae, 2004,22(4): 1103~1113. [doi: 10.5194/angeo-22-1103-2004]
    [6] Wang Q, Liang JM, Hu ZJ, Hu HH, Zhao H, Hu HQ, Gao XB, Yang HG. Spatial texture based automatic classification of dayside aurora in all-sky images. Journal of Atmospheric and Solar-Terrestrial Physics, 2010,72(5):498~508. [doi: 10.1016/j.jastp.2010.01. 011]
    [7] Fu R, Li J, Gao XB, Jian YJ. Automatic aurora images classification algorithm based on separated texture. In: Proc. of the 2009 IEEE Int’l Conf. on Robotics and Biomimetics (ROBIO). IEEE, 2009. 1331~1335. [doi: 10.1109/ROBIO.2009.5420722]
    [8] Yang X, Li J, Han B, Gao XB. Wavelet hierarchical model for aurora images classification. Journal of Xidian University, 2013, 40(2):18~24 (in Chinese with English abstract). [doi: 10.3969/j.issn.1001-2400.2013.02.004]
    [9] Han B, Qiu WL. Aurora images classification via features salient coding. Journal of Xidian University, 2013,40(6):180~186 (in Chinese with English abstract). [doi: 10.3969/j.issn.1001-2400.2013.06.030]
    [10] Han B, Zhao XJ, Tao DC, Li XL, Hu ZJ, Hu HQ. Dayside aurora classification via BIFs-based sparse representation using manifold learning. Int’l Journal of Computer Mathematics. [doi: 10.1080/00207160.2013.831084]
    [11] Han B, Yang C, Gao XB. Aurora image classification based on LDA combining with saliency information. Ruan Jian Xue Bao/ Journal of Software, 2013,24(11):2758~2766 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4481.htm [doi: 10.3724/SP.J.1001.2013.04481]
    [12] Liu H, Gao XB, Han B, Yang X. An automatic MSRM method with a feedback based on shape information for auroral oval segmentation. In: Proc. of the Intelligence Science and Big Data Engineering. Berlin, Heidelberg: Springer-Verlag, 2013. 748~755. [doi: 10.1007/978-3-642-42057-3_94]
    [13] Wang Q, Liang JM, Hu ZJ. Auroral event detection using spatiotemporal statistics of local motion vector. Chinese Journal of Polar Research, 2012,24(1):60~69 (in Chinese with English abstract). [doi: 10.3724/SP.J.1084.2012.00060]
    [14] Yang QJ, Liang JM, Hu ZJ, Xing ZY, Zhao H. Automatic recognition of poleward moving auroras from all-sky image sequences based on HMM and SVM. Planetary and Space Science, 2012,69:40~48. [doi: 10.1016/j.pss.2012.04.008]
    [15] Zhao GY, Pietikaèinen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2007,29(6):915~928. [doi: 10.1109/TPAMI,2007.1110]
    [16] Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(7):971~987. [doi: 10.1109/TPAMI.2002.1017623]
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韩冰,廖谦,高新波.基于空时极向LBP的极光序列事件检测.软件学报,2014,25(9):2172-2179

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History
  • Received:April 09,2014
  • Revised:May 14,2014
  • Online: September 09,2014
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