Abstract
Nonlinear spectral mixture analysis (NSMA) is a widely used unmixing algorithm. It can fit the mixed spectra adequately, but collinearity effect among true and virtual endmembers will decrease the retrieval accuracies of endmember fractions. Use of linear spectral mixture analysis (LSMA) can effectively reduce the degree of collinearity in the NSMA. However, the inadequate modeling of mixed spectra in the LSMA will also yield retrieval errors, especially for the cases where the multiple scattering is not ignorable. In this study, a generalized spectral unmixing scheme based on a spectral shape measure, i.e. spectral information divergence (SID), was applied to overcome the limitations of the conventional NSMA and LSMA. Two simulation experiments were undertaken to test the performances of the SID, LSMA and NSMA in the mixture cases of treesoil, tree-concrete and tree-grass. Results demonstrated that the SID yielded higher accuracies than the LSMA for almost all the mixture cases in this study. On the other hand, performances of the SID method were comparable with the NSMA for the tree-soil and tree-grass mixture cases, but significantly better than the NSMA for the tree-concrete mixture case. All the results indicate that the SID method is fairly effective to circumvent collinearity effect within the NSMA, and compensate the inadequate modeling of mixed spectra within the LSMA.
Highlights
Land surfaces are inherently heterogeneous at large scales, and mixed pixels exist widely in remotely sensedHow to cite this paper: Yang, W. and Kondoh, A. (2016) Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure
For the case of tree-concrete mixture, which would be encountered in urban areas, the Variance Inflation Factor (VIF) of Nonlinear spectral mixture analysis (NSMA) is about one hundred and twenty times larger than that of the linear spectral mixture analysis (LSMA) (167.0 vs. 1.4), showing that the degree of collinearity is dramatically increased by the NSMA
The results indicate that the degree of collinearity will be increased by the NSMA, while the amount of increase depends on the spectral shape of endmembers
Summary
Land surfaces are inherently heterogeneous at large scales, and mixed pixels exist widely in remotely sensedHow to cite this paper: Yang, W. and Kondoh, A. (2016) Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure. How to cite this paper: Yang, W. and Kondoh, A. Kondoh images due to coarse spatial resolutions. The spectral mixture results in errors for the materials’ discrimination and classification, and greatly hinders the development of quantitative remote sensing. On the other hand, understanding abundances (fractions) of components (endmembers) will greatly benefit the modeling of biogeochemical cycles and climate at both global and regional scales [1] [2]. It stimulates the development of spectral mixture analysis (SMA) techniques to quantify the fractions of the endmembers present in the mixed pixels
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