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
Conference Name2014 IEEE International Conference on Image Processing (ICIP)
Source PublicationIEEE
Conference Date27-30 Oct. 2014
Conference PlaceParis, France
Abstract
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.
KeywordHyperspectral Imagery Linear Mixing Model Nonnegative Matrix Factorization Spectral Cluster
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11965
Collection综合信息系统研究中心
Corresponding AuthorXinwei Jiang
AffiliationInstitute of Automation,Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jiang XW,Xinwei Jiang. CLUSTER CONSTRAINT BASED SPARSE NMF FOR HYPERSPECTRAL IMAGERY UNMIXING[C],2014.
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