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Spectral Unmixing via Data-Guided Sparsity
Zhu, Feiyun; Wang, Ying; Fan, Bin; Xiang, Shiming; Meng, Gaofeng; Pan, Chunhong
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2014-12-01
Volume23Issue:12Pages:5412-5427
SubtypeArticle
AbstractHyperspectral 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.
KeywordData-guided Sparse (Dgs) Data-guided Map (dgMap) Nonnegative Matrix Factorization (Nmf) Dgs-nmf Mixed Pixel Hyperspectral Unmixing (Hu)
WOS HeadingsScience & Technology ; Technology
WOS KeywordNONNEGATIVE MATRIX FACTORIZATION ; HYPERSPECTRAL DATA ; ENDMEMBER EXTRACTION ; ALGORITHM ; REPRESENTATION ; LIKELIHOOD ; SELECTION ; IMAGERY ; PARTS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000345235900001
Citation statistics
Cited Times:69[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3709
Collection模式识别国家重点实验室_先进数据分析与学习
AffiliationChinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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