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

Article

Abstract

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.

Keywords

Non-negative matrix factorization (NMF) Principal component analysis (PCA) Endmember Hyperspectral 

CLC number

TP751.1 

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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of ElectronicsChinese Academy of SciencesBeijingChina
  2. 2.MOE Key Laboratory for Earth System Modeling, Center for Earth System ScienceTsinghua UniversityBeijingChina
  3. 3.The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuangChina

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