Abstract
An unsupervised nonlinear decomposing algorithm for hyperspectral imagery was introduced to solve the nonlinear decomposing problem of hyperspectral imagery.The original data were mapped into a high-dimensional feature space by a nonlinear mapping,which was associated with a kernel function.Then the higher order relationships between the data were exploited.The mapped data became linearly separable in the high-dimensional feature space by using an appropriate nonlinear mapping.Then a linear nonnegative matrix factorization(NMF) method can be applied to extract more useful features.Endmember correlation coefficient,spectral angle distance,spectral information divergence and root mean square error were used to estimate the quality of the results.The experimental results of synthetic mixtures and a real image scene demonstrated that the method outperformed the nonnegative matrix factorization approach.
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