CASIA OpenIR  > 综合信息系统研究中心
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
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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