Authors
Jie Chen, Cédric Richard, Paul Honeine,
Title
A novel kernel-based nonlinear unmixing scheme of hyperspectral images
In
Proc. 45th Asilomar Conference on Signals, Systems, and Computers
Pages
1898–1902
Publisher
IEEE
Year
2011
Indexed by
Abstract
In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.
Affiliations
Offprint