  Spectral unmixing is an important task in hyperspectral image processing for
separating the mixed spectral data pertaining to various materials observed
individual pixels. Recently, nonlinear spectral unmixing has received
particular attention because a linear mixture is not appropriate under many
conditions. However, existing nonlinear unmixing approaches are often based on
specific assumptions regarding the inherent nonlinearity, and they can be
ineffective when applied to conditions deviating from the original assumptions.
Therefore, these approaches are not well suited to scenes with unknown
nonlinearity characteristics. This paper presents an unsupervised nonlinear
spectral unmixing method based on a deep autoencoder network that applies to a
generalized linear-mixture/nonlinear fluctuation model, consisting of a linear
mixture component and an additive nonlinear mixture component that depends on
both endmembers and abundances. The proposed approach benefits from the
universal modeling ability of deep neural networks to learn the inherent
nonlinearity of the nonlinear mixture component from the data itself via the
autoencoder network, rather than relying on an assumed form. Extensive
experiments with numerically synthetic, labeled laboratory-created data and
real airborne data, illustrate the generality and effectiveness of this
approach compared with state-of-the-art methods.
