Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data  

作　　者：Xiu-rui GENG Lu-yan JI Kang SUN 

机构地区：[1]Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China [2]MOE Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China [3]The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050000, China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2016年第5期403-412,共10页信息与电子工程前沿（英文版）

摘　　要：Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.Non-negative matrix factorization （NMF） has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings： （1） since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule; （2） NMF is sensitive to noise （outliers）, and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis （PCA） can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ＇principal component NMF＇ （PCNMF）. Experimental results show that PCNMF is both accurate and time-saving.

关 键 词：Non-negative matrix factorization(NMF) Principal component analysis(PCA) ENDMEMBER HYPERSPECTRAL 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]