Spectral Unmixing via Data-Guided Sparsity

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Spectral Unmixing via Data-Guided Sparsity
	Zhu, Feiyun; Wang, Ying; Fan, Bin; Xiang, Shiming; Meng, Gaofeng; Pan, Chunhong
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2014-12-01
卷号	23 期号:12 页码:5412-5427
文章类型	Article
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.
关键词	Data-guided Sparse (Dgs) Data-guided Map (dgMap) Nonnegative Matrix Factorization (Nmf) Dgs-nmf Mixed Pixel Hyperspectral Unmixing (Hu)
WOS标题词	Science & Technology ; Technology
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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000345235900001
引用统计	被引频次：192[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3709
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
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.

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