Spectral Unmixing via Data-Guided Sparsity
文献类型：期刊论文
| 作者 | Zhu, Feiyun; Wang, Ying ; Fan, Bin; Xiang, Shiming; Meng, Gaofeng; Pan, Chunhong
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2014-12-01 |
| 卷号 | 23期号:12页码:5412-5427 |
| 关键词 | Data-guided sparse (DgS) data-guided map (DgMap) nonnegative matrix factorization (NMF) DgS-NMF mixed pixel hyperspectral unmixing (HU) |
| 英文摘要 | Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging-both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel. Through this DgMap, the l(p) (0 < p < 1) constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What is more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the DgMap is feasible, and high quality unmixing results could be obtained by our method. |
| 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] | NONNEGATIVE MATRIX FACTORIZATION ; HYPERSPECTRAL DATA ; ENDMEMBER EXTRACTION ; ALGORITHM ; REPRESENTATION ; LIKELIHOOD ; SELECTION ; IMAGERY ; PARTS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000345235900001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3709]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 作者单位 | Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Feiyun,Wang, Ying,Fan, Bin,et al. Spectral Unmixing via Data-Guided Sparsity[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(12):5412-5427. |
| APA | Zhu, Feiyun,Wang, Ying,Fan, Bin,Xiang, Shiming,Meng, Gaofeng,&Pan, Chunhong.(2014).Spectral Unmixing via Data-Guided Sparsity.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(12),5412-5427. |
| MLA | Zhu, Feiyun,et al."Spectral Unmixing via Data-Guided Sparsity".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.12(2014):5412-5427. |
入库方式： OAI收割
来源：自动化研究所
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