  Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral
images as the product of endmember and abundance, which has been widely used in
hyperspectral imagery analysis. However, the influence of light, acquisition
conditions and the inherent properties of materials, results in that the
identified endmembers can vary spectrally within a given image (construed as
spectral variability). To address this issue, recent methods usually use a
priori obtained spectral library to represent multiple characteristic spectra
of the same object, but few of them extracted the spectral variability
explicitly. In this paper, a spectral variability augmented sparse unmixing
model (SVASU) is proposed, in which the spectral variability is extracted for
the first time. The variable spectra are divided into two parts of intrinsic
spectrum and spectral variability for spectral reconstruction, and modeled
synchronously in the SU model adding the regular terms restricting the sparsity
of abundance and the generalization of the variability coefficient. It is noted
that the spectral variability library and the intrinsic spectral library are
all constructed from the In-situ observed image. Experimental results over both
synthetic and real-world data sets demonstrate that the augmented decomposition
by spectral variability significantly improves the unmixing performance than
the decomposition only by spectral library, as well as compared to
state-of-the-art algorithms.
