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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (7): 542-549    DOI: 10.1631/jzus.C1000304
    
Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization
Ji-ming Li*,1,2, Yun-tao Qian1
1 School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2 Zhejiang Police College, Hangzhou 310053, China
Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization
Ji-ming Li*,1,2, Yun-tao Qian1
1 School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2 Zhejiang Police College, Hangzhou 310053, China
 全文: PDF(401 KB)  
摘要： 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.
关键词： Hyperspectral;  Band selection;  Clustering;  Sparse nonnegative matrix factorization    
Abstract: 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.
Key words: Hyperspectral    Band selection    Clustering    Sparse nonnegative matrix factorization
收稿日期: 2010-08-30 出版日期: 2011-07-04
CLC:  TP75  
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Ji-ming Li, Yun-tao Qian. Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization. Front. Inform. Technol. Electron. Eng., 2011, 12(7): 542-549.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000304    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I7/542

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