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期刊论文 2

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2016 2

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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

《信息与电子工程前沿（英文）》 2016年第17卷第3期页码 250-257 doi: 10.1631/FITEE.1500244

摘要： Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.

关键词： Endmember extraction Modified Cholesky factorization Spatial pixel purity index (SPPI) New simplex growing algorithm (NSGA) Kernel new simplex growing algorithm (KNSGA)

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

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

《信息与电子工程前沿（英文）》 2016年第17卷第5期页码 403-412 doi: 10.1631/FITEE.1600028

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.

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