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Manifold Regularized Sparse NMF for Hyperspectral Unmixing
文献类型：期刊论文
| 作者 | Lu, Xiaoqiang1 ; Wu, Hao2; Yuan, Yuan1; Yan, Pingkun1; Li, Xuelong1
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| 刊名 | ieee transactions on geoscience and remote sensing
 |
| 出版日期 | 2013-05-01 |
| 卷号 | 51期号:5页码:2815-2826 |
| 关键词 | Hyperspectral unmixing manifold regularization mixed pixel nonnegative matrix factorization (NMF) |
| 英文摘要 | hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. recently, many sparse nonnegative matrix factorization (nmf) algorithms have achieved advanced performance for hyperspectral unmixing because they overcome the difficulty of absence of pure pixels and sufficiently utilize the sparse characteristic of the data. however, most existing sparse nmf algorithms for hyperspectral unmixing only consider the euclidean structure of the hyperspectral data space. in fact, hyperspectral data are more likely to lie on a low-dimensional submanifold embedded in the high-dimensional ambient space. thus, it is necessary to consider the intrinsic manifold structure for hyperspectral unmixing. in order to exploit the latent manifold structure of the data during the decomposition, manifold regularization is incorporated into sparsity-constrained nmf for unmixing in this paper. since the additional manifold regularization term can keep the close link between the original image and the material abundance maps, the proposed approach leads to a more desired unmixing performance. the experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art approaches. |
| WOS标题词 | science & technology ; physical sciences ; technology |
| 类目[WOS] | geochemistry & geophysics ; engineering, electrical & electronic ; remote sensing ; imaging science & photographic technology |
| 研究领域[WOS] | geochemistry & geophysics ; engineering ; remote sensing ; imaging science & photographic technology |
| 关键词[WOS] | nonnegative matrix factorization ; endmember extraction ; component analysis ; algorithm ; imagery |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000318428700025 |
| 公开日期 | 2015-06-30 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/24009]  |
| 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 | 1.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 2.Hubei Univ, Fac Math & Comp Sci, Wuhan 430062, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Xiaoqiang,Wu, Hao,Yuan, Yuan,et al. Manifold Regularized Sparse NMF for Hyperspectral Unmixing[J]. ieee transactions on geoscience and remote sensing,2013,51(5):2815-2826. |
| APA | Lu, Xiaoqiang,Wu, Hao,Yuan, Yuan,Yan, Pingkun,&Li, Xuelong.(2013).Manifold Regularized Sparse NMF for Hyperspectral Unmixing.ieee transactions on geoscience and remote sensing,51(5),2815-2826. |
| MLA | Lu, Xiaoqiang,et al."Manifold Regularized Sparse NMF for Hyperspectral Unmixing".ieee transactions on geoscience and remote sensing 51.5(2013):2815-2826. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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