Abstract
The mysterious and beautiful aurora represents various physical meaning, thus the classification of aurora images have significant scientific value to human beings. Principal component analysis network (PCANet) has achieved good results in classification. But when using PCANet to extract the image features, it transform original image into a vector, so that the structure information of the image are missing. Compared with PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so that 2DPCA can use the structure information of original image more efficiently and reduce the computational complexity. Based on PCANet, we develop a classification method of aurora images, 2-dimension PCANet (2DPCANet). To evaluate 2DPCANet performance, a series of experiments were performed on two different aurora databases. The classification rate across all experiments was higher using 2DPCANet than PCANet. The experiment results also indicated that the classification time is shorter using 2DPCANet than PCANet.
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Akasofu, S.I.: The development of the auroral substorm. Planetary and Space Science 12(4), 273–282 (1964). doi:10.1016/0032-0633(64)90151-5
Hu, H.Q., Liu, R.Y., Wang, J.F., Yang, H.G.: Statistic characteristics of the aurora observed at Zhongshan Stantion. Antarctica. Chinese Journal of Polar Research 11(1), 8–18 (1999). (in Chinese with English abstract)
Hu, Z.J., 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 71(8–9), 794–804 (2009). doi:10.1016/j.jastp.2009.02.010
Syrjäsuo, M.T., Donovan, E.F.: Diurnal auroral occurrence statistics obtained via machine vision. Annales Geophysicae 22(4), 1103–1113 (2004). doi:10.5194/angeo-22-1103-2004
Wang, Q., Liang, J.M., Gao, X.B., Yang, H.G., Hu, H.Q., Hu, Z.J.: Representation feature based aurora image classification method research. In: Proc. of the 12th National Solar-Terrestrial Space Physics Academic Conf., vol. 71 (2007)
Gao, L.J.: Dayside aurora classification based on gabor wavelet transformation. MS. Thesis. Xi’an: Xidian University (2009)
Fu, R., Li, J., Gao, X.B., Jian, Y.J.: Automatic aurora images classification algorithm based on separated texture. In: Proc. of the 2009 IEEE Int’l Conf. on Robotics and Biomimetics (ROBIO), pp. 1331−1335. IEEE (2009). doi:10.1109/ROBIO.2009.5420722
Wang, Y.R., Gao, X.B., Fu, R., Jian, Y.J.: Dayside corona aurora classification based on X-gray level aura matrices. In: Proc. of the ACM Int’l Conf. on Image and Video Retrieval, pp. 282−287. ACM (2010). doi:10.1145/1816041.1816083
Wang, Y.R.: Dayside aurora classification based on X-gray level aura matrices. MS. Thesis. Xi’an: Xidian University (2011) (in Chinese with English abstract)
Han, B., Qiu, W.L.: Aurora images classification via features salient coding. Journal of Xidian University 40(6), 1001–2400 (2013). doi:10.3969/j.issn.1001-2400.2013.06.030
Han, B., Yang, C., Gao, X.B.: Aurora image classification based on LDA combining with saliency information. Ruan Jian Xue Bao Journal of Software 24(11), 2758–2766 (2013). (in chinese) http://www.jos.org.cn/1000-9825/4481.html
Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: ICML (2013)
Kavukcuoglu, K., Sermanet, P., Boureau, Y., Gregor, K., Mathieu, M., LeCun, Y.: Learning convolutional feature hierarchies for visual recognition. In: NIPS (2010)
Jolliffe, I.T.: Principal component analysis, 2nd edn. Springer, New York (2002)
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: A Simple Deep Learning Baseline for Image Classification? Posted on arXiv.org and submitted to TIP, August 2014
Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Patt. Anal. Mach. Intell. 26(1), 131–137 (2004)
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Jia, Z., Han, B., Gao, X. (2015). 2DPCANet: Dayside Aurora Classification Based on Deep Learning. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48570-5_32
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DOI: https://doi.org/10.1007/978-3-662-48570-5_32
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