一种分层小波模型下的极光图像分类算法

doi:10.3969/j.issn.1001-2400.2013.02.004

Abstract

Abstract:

In order to improve the accuracy of aurora images classification, an algorithm based on the wavelet hierarchical model is proposed. In the proposed algorithm, the global and local wavelet features are extracted hierarchically first, then reduced in dimensions through the principal component analysis and used to classify the arc and three corona aurora images by the use of the support vector machine. By comparing the classification accuracy and time consumption, the optimal parameters in the wavelet hierarchical model are experimentally obtained and the validity of principal component analysis in feature optimization is verified. Experimental results show that the proposed algorithm improves the classification accuracy to a great degree with an acceptable time consumption compared with classical algorithms. Classification results between each two types of aurora images also provide some potential ways to improve the accuracy.

Key words: aurora image classification, wavelet hierarchical model, principal component analysis (PCA), support vector machine (SVM)

CLC Number:

TN911.73

YANG Xi;LI Jie;HAN Bing;GAO Xinbo. Wavelet hierarchical model for aurora images classification[J].J4, 2013, 40(2): 18-24.

References

［1］胡泽骏, 杨惠根, 艾勇, 等. 日侧极光卵的可见光多波段观测特征-中国北极黄河站首次极光观测初步分析［J］. 极地研究, 2005, 17(2):107-114.
Hu Zejun, Yang Huigen, Ai Yong, et al. Multiple Wavelengths Observation of Dayside Auroras in Visible Range-A Preliminary Result of the First Wintering Aurora Observation in Chinese Arctic Station at Ny-(A)Lesund ［J］. Chinese Journal of Polar Research, 2005, 17(2): 107-114.
［2］ Stomer C. The Polar Aurora ［M］. Oxford: Clarend on Press, 1955.
［3］ Syrjasuo M T, Donovan E F. Diurnal Auroral Occurrence Statistics Obtained via Machine Vision ［J］. Annales Geophysicae, 2004, 22(4):1103-1113.
［4］ Syrjasuo M T, Donovan E F, Qin X, et al. Automatic Classification of Auroral Images in Substorm Studies ［C］//Proc Eight International Conference on Substorms (ICS8). Alberta: University of Calgary, 2007: 309-313.
［5］ Wang Y R, Gao X B, Fu R, et al. Dayside Corona Aurora Classification Based on X-gray Level Aura Matrices ［C］//Proc ACM International Conference on Image and Video Retrieval (CIVR2010). Xi’an: Xidian University, 2010: 282-287.
［6］高凌君, 高新波, 梁继民. 结合样本选择和AdaBoost的日侧冕状极光检测算法［J］. 中国图象图形学报A, 2010, 15(1): 116-121.
Gao Lingjun, Gao Xinbo, Liang Jimin. Dayside Corona Aurora Detection Based on Sample Selection and AdaBoost Algorithm ［J］. Journal of Image and Graphics (A), 2010, 15(1): 116-121.
［7］付蓉, 李洁, 高新波. 基于形态学成分分析的静态极光图像分类算法［J］. 光子学报, 2010, 39(6): 1034-1039.
Fu Rong, Li Jie, Gao Xinbo. Static Aurora Images Classification Based on Morphological Component Analysis ［J］. Acta Photonica Sinica, 2010, 39(6): 1034-1039.
［8］李亚标, 王宝光, 李温温. 基于小波变换的图像纹理特征提取方法及其应用［J］. 传感技术学报, 2009, 22(9): 1308-1311.
Li Yabiao, Wang Baoguang, Li Wenwen. A Method of Image Texture Feature Analysis Based on Wavelet Decomposition and Its Application ［J］. Chinese Journal of Sensors and Actuators, 2009, 22(9): 1308-1311.
［9］李传伟, 李安宗. 一种小波变换信号处理方法［J］. 西安电子科技大学学报, 2009, 36(4): 751-755.
Li Chuanwei, Li Anzong. Algorithms and Application of the Wavelet-Based Signal Processing ［J］. Journal of Xidian University, 2009, 36(4): 751-755.
［10］ Pun C M, Lee M C. Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 590-603.
［11］ Martinez A M, Kak A C. PCA Versus LDA ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 3(2): 228-233.
［12］温浩, 卢朝阳, 高全学. 融合小波变换和张量PCA的人脸识别算法［J］. 西安电子科技大学学报, 2009, 36(4): 602-607.
Wen Hao, Lu Chaoyang, Gao Quanxue. Face Recognition Algorithm Based on Wavelet Preprocessing and Tensor PCA ［J］. Journal of Xidian University, 2009, 36(4): 602-607.
［13］ Hsu C W, Lin C J. A Comparison of Methods for Multiclass Support Vector Machines ［J］. IEEE Trans on Neural Networks, 2002, 13(2): 415-425.
［14］常甜甜, 刘红卫, 冯筠. 多源性数据SVM集成算法研究［J］. 西安电子科技大学学报, 2010, 37(1): 136-141.
Chang Tiantian, Liu Hongwei, Feng Jun. Support Vector Machine Ensemble Learning Algorithm Research Based on Heterogeneous Data ［J］. Journal of Xidian University, 2010, 37(1): 136-141.

[1]	WANG Junxiang,HUANG Lin,ZHANG Ying,NI Jiangqun,LIN Lang. Algorithm for the detection of a low complexity contrast enhanced image source [J]. Journal of Xidian University, 2021, 48(1): 96-106.
[2]	. A Wavelet Hierarchical Model for Aurora Images Classification [J]. J4, 2013, 40(2): 18-24.
[3]	YAO Yong;ZHAO Hui;LIU Zhi-jing. Multi-class classifier of non-linear SVM decision tree [J]. J4, 2007, 34(6): 873-876.

Wavelet hierarchical model for aurora images classification

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 3

Metrics

Comments

Recommended 0