Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
文献类型：会议论文
| 作者 | He, Shiying; Zhou, Haiwei; Wang Y(王尧); Cao, Wenfei; Han Z(韩志)
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| 出版日期 | 2016 |
| 会议名称 | 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 |
| 会议日期 | July 10-15, 2016 |
| 会议地点 | Beijing, China |
| 关键词 | Hyperspectral images Super-resolution reconstruction nuclear norm Folded-concave penalty 3D totalvariation |
| 页码 | 6962-6965 |
| 通讯作者 | He, Shiying |
| 中文摘要 | In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images. Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation (TV) to characterize the local spatial-and-spectral smoothness across all hyperspectral bands. Then, we develop an efficient algorithm for solving the resulting optimization problem by combing the local linear approximation (LLA) strategy and alternative direction method of multipliers (ADMM). Experimental results on one hyperspectral image dataset illustrate the merits of the proposed approach. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | The Institute of Electrical and Electronics Engineers, Geoscience and Remote Sensing Society (GRSS) |
| 会议录 | 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-5090-3332-4 |
| WOS记录号 | WOS:000388114606192 |
| 源URL | [http://ir.sia.cn/handle/173321/19769]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | He, Shiying,Zhou, Haiwei,Wang Y,et al. Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization[C]. 见:2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016. Beijing, China. July 10-15, 2016. |
入库方式： OAI收割
来源：沈阳自动化研究所
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