Double Constrained NMF for Hyperspectral Unmixing
文献类型：期刊论文
| 作者 | Lu, Xiaoqiang1 ; Wu, Hao2; Yuan, Yuan1
|
| 刊名 | ieee transactions on geoscience and remote sensing
 |
| 出版日期 | 2014-05-01 |
| 卷号 | 52期号:5页码:2746-2758 |
| 关键词 | Clustering-based regularization hyperspectral unmixing mixed pixel nonnegative matrix factorization (NMF) |
| ISSN号 | 0196-2892 |
| 英文摘要 | given only the collected hyperspectral data, unmixing aims at obtaining the latent constituent materials and their corresponding fractional abundances. recently, many nonnegative matrix factorization (nmf)-based algorithms have been developed to deal with this issue. considering that the abundances of most materials may be sparse, the sparseness constraint is intuitively introduced into nmf. although sparse nmf algorithms have achieved advanced performance in unmixing, the result is still susceptible to unstable decomposition and noise corruption. to reduce the aforementioned drawbacks, the structural information of the data is exploited to guide the unmixing. since similar pixel spectra often imply similar substance constructions, clustering can explicitly characterize this similarity. through maintaining the structural information during the unmixing, the resulting fractional abundances by the proposed algorithm can well coincide with the real distributions of constituent materials. moreover, the additional clustering-based regularization term also lessens the interference of noise to some extent. the experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art algorithms. |
| 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:000332484700038 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22374]  |
| 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, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, 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. Double Constrained NMF for Hyperspectral Unmixing[J]. ieee transactions on geoscience and remote sensing,2014,52(5):2746-2758. |
| APA | Lu, Xiaoqiang,Wu, Hao,&Yuan, Yuan.(2014).Double Constrained NMF for Hyperspectral Unmixing.ieee transactions on geoscience and remote sensing,52(5),2746-2758. |
| MLA | Lu, Xiaoqiang,et al."Double Constrained NMF for Hyperspectral Unmixing".ieee transactions on geoscience and remote sensing 52.5(2014):2746-2758. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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