CLUSTER CONSTRAINT BASED SPARSE NMF FOR HYPERSPECTRAL IMAGERY UNMIXING

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	CLUSTER CONSTRAINT BASED SPARSE NMF FOR HYPERSPECTRAL IMAGERY UNMIXING
	Jiang XW(蒋心为); Xinwei Jiang
	2014-12
会议日期	27-30 Oct. 2014
会议地点	Paris, France
关键词	Hyperspectral Imagery Linear Mixing Model Nonnegative Matrix Factorization Spectral Cluster
英文摘要	Nonnegative matrix factorization(NMF) has been applied to hyperspectral unmixing in recent years. Different constraints based on geometrical or statistical properties of endmember and abundance are incorporated into NMF model to improve unmixing result. In this paper, a new regularizer based on spectral cluster information is proposed to strengthen the constrained relationship between original image and abundance maps. The new algorithm makes abundances of similar pixels close and abundances of dissimilar pixels be separated completely. Additionally, L1/2 sparsity constraint is adopted to make the solutions sparse. Comparative results on real and synthetic hyperspectral datasets prove our proposed method could improve the hyperspectral unmixing accuracy.
会议录	IEEE
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11965]
专题	自动化研究所_综合信息系统研究中心
通讯作者	Xinwei Jiang
作者单位	Institute of Automation,Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Jiang XW,Xinwei Jiang. CLUSTER CONSTRAINT BASED SPARSE NMF FOR HYPERSPECTRAL IMAGERY UNMIXING[C]. 见:. Paris, France. 27-30 Oct. 2014.