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CLC number: TP75

On-line Access: 2011-07-04

Received: 2010-08-30

Revision Accepted: 2010-11-30

Crosschecked: 2011-05-31

Cited: 6

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.7 P.542-549


Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization

Author(s):  Ji-ming Li, Yun-tao Qian

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China, Zhejiang Police College, Hangzhou 310053, China

Corresponding email(s):   ljming@zju.edu.cn

Key Words:  Hyperspectral, Band selection, Clustering, Sparse nonnegative matrix factorization

Ji-ming Li, Yun-tao Qian. Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization[J]. Journal of Zhejiang University Science C, 2011, 12(7): 542-549.

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author="Ji-ming Li, Yun-tao Qian",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization
%A Ji-ming Li
%A Yun-tao Qian
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 7
%P 542-549
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000304

T1 - Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization
A1 - Ji-ming Li
A1 - Yun-tao Qian
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 7
SP - 542
EP - 549
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000304

hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands. band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image analysis. In this paper, we propose a new band selection algorithm using sparse nonnegative matrix factorization (sparse NMF). Though acting as a clustering method for band selection, sparse NMF need not consider the distance metric between different spectral bands, which is often the key step for most common clustering-based band selection methods. By imposing sparsity on the coefficient matrix, the bands’ clustering assignments can be easily indicated through the largest entry in each column of the matrix. Experimental results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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