Structured Sparse Method for Hyperspectral Unmixing
文献类型：期刊论文
| 作者 | Zhu, Feiyun; Wang, Ying ; Xiang, Shiming; Fan, Bin; Pan, Chunhong
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| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2014-02-01 |
| 卷号 | 88页码:101-118 |
| 关键词 | Hyperspectral Unmixing (HU) Hyperspectral image analysis Structured Sparse NMF (SS-NMF) Mixed pixel Nonnegative Matrix Factorization (NMF) |
| 英文摘要 | Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
| 类目[WOS] | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 研究领域[WOS] | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 关键词[WOS] | NONNEGATIVE MATRIX FACTORIZATION ; ENDMEMBER EXTRACTION ALGORITHM ; CONSTRAINED LEAST-SQUARES ; COMPONENT ANALYSIS ; IMAGERY ; QUANTIFICATION ; REPRESENTATION ; GRADIENT ; PARTS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000331921200010 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3714]  |
| 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, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Feiyun,Wang, Ying,Xiang, Shiming,et al. Structured Sparse Method for Hyperspectral Unmixing[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2014,88:101-118. |
| APA | Zhu, Feiyun,Wang, Ying,Xiang, Shiming,Fan, Bin,&Pan, Chunhong.(2014).Structured Sparse Method for Hyperspectral Unmixing.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,88,101-118. |
| MLA | Zhu, Feiyun,et al."Structured Sparse Method for Hyperspectral Unmixing".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 88(2014):101-118. |
入库方式： OAI收割
来源：自动化研究所
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