| Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection | |
| Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang; 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. | |
| 作者部门 | 光学影像学习与分析中心 |
| 2017 | |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| 卷号 | 26期号:1页码:51-64 |
| 产权排序 | 1 |
| 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. |
| 文章类型 | Article |
| 关键词 | Hyperspectral Band Selection Multiple Graphs Determinantal Point Process Hyperspectral Image Classification Anomaly Detection Target Detection |
| 学科领域 | Computer Science, Artificial Intelligence |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TIP.2016.2617462 |
| 收录类别 | SCI |
| 关键词[WOS] | PARTICLE SWARM OPTIMIZATION ; IMAGE CLASSIFICATION ; MUTUAL-INFORMATION ; FACE RECOGNITION ; TARGET DETECTION ; REDUNDANCY ; SUBSPACE ; PATTERN ; GRAPH |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| 项目资助者 | National Basic Research Program of China (Youth 973 Program)(2013CB336500) ; State Key Program of National Natural Science of China(61232010) ; National Natural Science Foundation of China(61472413) ; Key Research Program of the Chinese Academy of Sciences(KGZD-EWT03) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) ; Young Top-Notch Talent Program of Chinese Academy of Sciences(QYZDB-SSW-JSC015) |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000402822500004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/29040 |
| 专题 | 光谱成像技术研究室 |
| 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. |
| 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,et al. 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,&Yuan, Y .(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. |
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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 | ||
| Discovering Diverse （3800KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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