NDLI: Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization

Content Provider	IEEE Xplore Digital Library
Author	Zuyuan Yang Guoxu Zhou Shengli Xie Shuxue Ding Jun-Mei Yang Jun Zhang
Copyright Year	1992
Abstract	Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.
Sponsorship	IEEE Signal Processing Society
Page Count	14
File Size	7212247
Starting Page	1112
Ending Page	1125
File Format	PDF
ISSN	10577149
Volume Number	20
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Algorithm design and analysis Hyperspectral imaging Estimation Noise Data models Source separation sparseness measure Blind spectral unmixing nonnegative matrix factorization (NMF)
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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