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
会议名称	2014 IEEE International Conference on Image Processing (ICIP)
会议录名称	IEEE
会议日期	27-30 Oct. 2014
会议地点	Paris, France
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.
关键词	Hyperspectral Imagery Linear Mixing Model Nonnegative Matrix Factorization Spectral Cluster
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11965
专题	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
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jiang XW,Xinwei Jiang. CLUSTER CONSTRAINT BASED SPARSE NMF FOR HYPERSPECTRAL IMAGERY UNMIXING[C],2014.

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