Extreme-constrained spatial-spectral corner detector for image-level hyperspectral image classification
文献类型:期刊论文
作者 | Li, Yanshan1,2,6; Xu, Jianjie1; Xia, Rongjie1; Huang, Qinghua1,3,4; Xie, Weixin1; Li, Xuelong5; Huang, QH (reprint author), Shenzhen Univ, Coll Informat Engn, ATR Natl Key Lab Def Technol, Shenzhen 518060, Peoples R China. |
刊名 | PATTERN RECOGNITION LETTERS
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出版日期 | 2018-07-15 |
卷号 | 109页码:110-119 |
ISSN号 | 0167-8655 |
DOI | 10.1016/j.patrec.2018.03.022 |
产权排序 | 4 |
文献子类 | Article |
英文摘要 | As one type of local invariant feature, corner feature plays an important role in diverse applications such as: video mining, target detection, image classification, image retrieval, and image matching, etc. However, there are few studies on corner feature for hyperspectral image (HSI). Therefore, this paper proposes a novel corner feature for HSI named extreme-constrained spatial-spectral corner (ECSSC for short) and its corresponding detector. The definition of ECSSC is developed based on the definition of spectral-spatial interest point and the characteristic of HSI. Based on this definition, the detector of ECSSC is put forward and introduced in detail. Then, as an important application of ECSSC, an efficient framework for image-level HSI classification is designed based on ECSSC and parallel computation. The experimental results show that the proposed algorithm can detect abundant corner features with high repeatability rate from HSI and the accuracy of image-level HSI based on ECSSC is dramatically higher than that of the state of the art. |
学科主题 | Computer Science, Artificial Intelligence |
WOS关键词 | Face Recognition ; Feature-extraction ; Model ; Information ; Saturation ; Regression ; Kernels ; Quality |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000434380800015 |
资助机构 | National Natural Science Foundation of China(61771319 ; Natural Science Foundation of Guangdong Province(2017A030313343 ; Shenzhen Science and Technology Project(JCYJ20160520173822387 ; Project of Science and Technology Department of Guangdong Province(2014A050503020 ; Science and Technology Program of Guangzhou(201704020134) ; 61372007 ; 2017A030312006) ; JCYJ20160307143441261) ; 2016A010101021 ; 61571193) ; 2016A010101022 ; 2016A010101023) |
源URL | [http://ir.opt.ac.cn/handle/181661/30342] ![]() |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Huang, QH (reprint author), Shenzhen Univ, Coll Informat Engn, ATR Natl Key Lab Def Technol, Shenzhen 518060, Peoples R China. |
作者单位 | 1.Shenzhen Univ, Coll Informat Engn, ATR Natl Key Lab Def Technol, Shenzhen 518060, Peoples R China 2.China Three Gorges Univ, Hubei Key Lab Intelligent Vis Based Monitoring Hy, Yichang 443002, Peoples R China 3.Northwestern Polytech Univ, Sch Mech Engn, Xian 710072, Shaanxi, Peoples R China 4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China 6.Shenzhen Univ, Coll Informat Engn, Guangdong Key Lab Intelligent Informat Proc, Shenzhen, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Yanshan,Xu, Jianjie,Xia, Rongjie,et al. Extreme-constrained spatial-spectral corner detector for image-level hyperspectral image classification[J]. PATTERN RECOGNITION LETTERS,2018,109:110-119. |
APA | Li, Yanshan.,Xu, Jianjie.,Xia, Rongjie.,Huang, Qinghua.,Xie, Weixin.,...&Huang, QH .(2018).Extreme-constrained spatial-spectral corner detector for image-level hyperspectral image classification.PATTERN RECOGNITION LETTERS,109,110-119. |
MLA | Li, Yanshan,et al."Extreme-constrained spatial-spectral corner detector for image-level hyperspectral image classification".PATTERN RECOGNITION LETTERS 109(2018):110-119. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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