NDLI: Minimum endmember-wise distance constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of hyperspectral images

Content Provider	IEEE Xplore Digital Library
Author	Shaohui Mei Mingyi He
Copyright Year	2011
Description	Author affiliation: School of Electronics and Information, Northwestern Polytechnical University, Shaanxi Key Laboratory of Information Acquisition and Processing, Xi'an, 710129, China (Shaohui Mei; Mingyi He)
Abstract	Nonnegative Matrix Factorization (NMF) and its extensions have gained lots of attentions in Spectral Mixture Analysis (SMA) since they can handle highly mixed hyperspectral pixels in an unsupervised way. In order to overcome the non-uniqueness problem in NMF, a minimum endmember-wise distance constraint (MewDC), which optimizes endmember spectra as compact as possible, is imposed for satisfying unmixing results. The proposed constraint works similar to minimum volume constraint (MVC). However, the dimension reduction step and numerical instability problems in MVC can be avoided. As a result, a minimum endmember-wise distance constrained NMF (MewDC-NMF) algorithm is proposed to extract endmembers and estimate their corresponding fractional abundance simultaneously. Both synthetic and real hyperspectral data experiments have demonstrate the effectiveness of the proposed MewDC-NMF algorithm.
Starting Page	1299
Ending Page	1302
File Size	520763
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049438
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Algorithm design and analysis Minerals Optimization Nonnegative Matrix Factorization Spectral Mixture Analysis Endmember extraction Abundance Estimation
Content Type	Text
Resource Type	Article

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