Dayside aurora classification via BIFs-based sparse representation using manifold learning | |
Han, Bing1,2; Zhao, Xiaojing1,2; Tao, Dacheng3; Li, Xuelong4; Hu, Zejun5; Hu, Hongqiao5 | |
作者部门 | 光学影像学习与分析中心 |
2014-11-02 | |
发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS |
ISSN | 0020-7160 |
卷号 | 91期号:11页码:2415-2426 |
产权排序 | 4 |
摘要 | Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity. A new aurora classification algorithm based on biologically inspired features (BIFs) and discriminative locality alignment (DLA) is proposed in this paper. First, an aurora image is represented by the BIFs, which combines the C1 units from the hierarchical model of object recognition in cortex and the gist features from the saliency map; then, the manifold learning method called DLA is used to obtain the effective sparse representation for auroras based on BIFs; finally, classification results using support vector machine and nearest neighbour with three sets of features: the C1 unit features, the gist features and the BIFs illustrate the effectiveness and robustness of our method on the real aurora image database from Chinese Arctic Yellow River Station. |
文章类型 | Article |
关键词 | 65d19 65f05 62h30 68u10 C1 Unit Features Dayside Aurora Classification Biologically Inspired Features Gist Features Discriminative Locality Alignment |
WOS标题词 | Science & Technology ; Physical Sciences |
DOI | 10.1080/00207160.2013.831084 |
收录类别 | SCI ; EI |
关键词[WOS] | CORTEX-LIKE MECHANISMS ; MACAQUE VISUAL-CORTEX ; SPATIAL-FREQUENCY ; SALIENCY ; SCENE ; RECOGNITION ; SELECTIVITY ; CELLS |
语种 | 英语 |
WOS研究方向 | Mathematics |
项目资助者 | National Natural Science Foundation of China(41031064 ; Shaanxi Province Natural Science Fundamental Research Funded Projects(2011JQ8019) ; Special Scientific Research of Marine Public Welfare Industry(201005017) ; Basic Foundation for Scientific Research ; Fundamental Research Funds for the Central Universities(K5051302008) ; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry ; 60902082) |
WOS类目 | Mathematics, Applied |
WOS记录号 | WOS:000345272500008 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/22418 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Xidian Univ, Sch Elect Engn, VIPS Lab, Xian 710071, Peoples R China 2.Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China 3.Univ Technol Sydney, Sydney, NSW 2007, Australia 4.Xian Inst Opt & Precis Mech CAS, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 5.Polar Res Inst China, SOA Key Lab Polar Sci, Shanghai 200136, Peoples R China |
推荐引用方式 GB/T 7714 | Han, Bing,Zhao, Xiaojing,Tao, Dacheng,et al. Dayside aurora classification via BIFs-based sparse representation using manifold learning[J]. INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS,2014,91(11):2415-2426. |
APA | Han, Bing,Zhao, Xiaojing,Tao, Dacheng,Li, Xuelong,Hu, Zejun,&Hu, Hongqiao.(2014).Dayside aurora classification via BIFs-based sparse representation using manifold learning.INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS,91(11),2415-2426. |
MLA | Han, Bing,et al."Dayside aurora classification via BIFs-based sparse representation using manifold learning".INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 91.11(2014):2415-2426. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Dayside aurora class(459KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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