TITLE:
Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure
AUTHORS:
Wei Yang, Akihiko Kondoh
KEYWORDS:
Nonlinear Spectral Mixture Analysis, Linear Spectral Mixture Analysis, Collinearity, Spectral Information Divergence (SID)
JOURNAL NAME:
Advances in Remote Sensing,
Vol.5 No.3,
August
15,
2016
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.