Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization  

作　　者：Ji-ming LI 1,2,Yun-tao QIAN 1 (1 School of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China) (2 Zhejiang Police College,Hangzhou 310053,China) 

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2011年第7期542-549,共8页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project (No.60872071) supported by the National Natural Science Foundation of China

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.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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]