Author:

Keywords:

Science & Technology, Physical Sciences, Technology, Geochemistry & Geophysics, Engineering, Electrical & Electronic, Remote Sensing, Imaging Science & Photographic Technology, Engineering, Collinearity problem, hyperspectral data, linear spectral mixture analysis (LSMA), nonlinear spectral mixture analysis (NSMA), spectral mixture analysis (SMA), NONNEGATIVE MATRIX FACTORIZATION, NEURAL-NETWORK APPROACH, MIXTURE ANALYSIS, LAND-COVER, HYPERSPECTRAL IMAGERY, MULTISPECTRAL IMAGES, MULTIPLE-REGRESSION, VEGETATION, SOIL, EXTRACTION, 0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering, Geological & Geomatics Engineering, 37 Earth sciences, 40 Engineering

Abstract:

In the past decades, spectral unmixing has been studied for deriving the fractions of spectrally pure materials in a mixed pixel. However, limited attention has been given to the collinearity problem in spectral mixture analysis. In this paper, quantitative analysis and detailed simulations are provided, which show that the high correlation between the endmembers, including the virtual endmembers introduced in a nonlinear model, has a strong impact on unmixing errors through inflating the Gaussian noise. While distinctive spectra with low correlations are often selected as true endmembers, the virtual endmembers formed by their product terms can be highly correlated. It is found that a virtual-endmember-based nonlinear model generally suffers more from collinearity problems compared to linear models and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted on a set of in situ measured data, and the results show that the linear mixture model performs better in 61.5% of the cases. © 2011 IEEE.