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Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection
文献类型：期刊论文
| 作者 | Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang |
| 刊名 | ieee transactions on image processing
 |
| 出版日期 | 2017 |
| 卷号 | 26期号:1页码:51-64 |
| ISSN号 | 1057-7149 |
| 关键词 | Hyperspectral band selection multiple graphs determinantal point process hyperspectral image classification anomaly detection target detection |
| 通讯作者 | yuan, y (reprint author), 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. |
| 产权排序 | 1 |
| 英文摘要 | band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. this has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden. though hyperspectral band selection has gained rapid development in recent years, it is still a challenging task because of the following requirements: 1) an effective model can capture the underlying relations between different high-dimensional spectral bands; 2) a fast and robust measure function can adapt to general hyperspectral tasks; and 3) an efficient search strategy can find the desired selected bands in reasonable computational time. to satisfy these requirements, a multigraph determinantal point process (mdpp) model is proposed to capture the full structure between different bands and efficiently find the optimal band subset in extensive hyperspectral applications. there are three main contributions: 1) graphical model is naturally transferred to address band selection problem by the proposed mdpp; 2) multiple graphs are designed to capture the intrinsic relationships between hyperspectral bands; and 3) mixture dpp is proposed to model the multiple dependencies in the proposed multiple graphs, and offers an efficient search strategy to select the optimal bands. to verify the superiority of the proposed method, experiments have been conducted on three hyperspectral applications, such as hyperspectral classification, anomaly detection, and target detection. the reliability of the proposed method in generic hyperspectral tasks is experimentally proved on four real-world hyperspectral data sets. |
| 学科主题 | computer science, artificial intelligence ; engineering, electrical & electronic |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | particle swarm optimization ; image classification ; mutual-information ; face recognition ; target detection ; redundancy ; subspace ; pattern ; graph |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000402822500004 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/29040]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 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 |
| 推荐引用方式 GB/T 7714 | Yuan, Yuan,Zheng, Xiangtao,Lu, Xiaoqiang. Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection[J]. ieee transactions on image processing,2017,26(1):51-64. |
| APA | Yuan, Yuan,Zheng, Xiangtao,&Lu, Xiaoqiang.(2017).Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection.ieee transactions on image processing,26(1),51-64. |
| MLA | Yuan, Yuan,et al."Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection".ieee transactions on image processing 26.1(2017):51-64. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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