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Journal Article 2

Year

2016 2

Keywords

Endmember 1

Endmember extraction 1

Hyperspectral 1

Kernel new simplex growing algorithm (KNSGA) 1

Modified Cholesky factorization 1

New simplex growing algorithm (NSGA) 1

Non-negative matrix factorization (NMF) 1

Principal component analysis (PCA) 1

Spatial pixel purity index (SPPI) 1

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Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember

Jing LI,Xiao-run LI,Li-jiao WANG,Liao-ying ZHAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3, Pages 250-257 doi: 10.1631/FITEE.1500244

Abstract: Endmember extraction is a key step in the hyperspectral image analysis process.developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmemberfirst issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember

Keywords： Endmember extraction Modified Cholesky factorization Spatial pixel purity index (SPPI) New simplex

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Non-negativematrix factorization based unmixing for principal component transformed hyperspectral data

Xiu-rui GENG,Lu-yan JI,Kang SUN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5, Pages 403-412 doi: 10.1631/FITEE.1600028

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