  In this study, we propose a novel framework for hyperspectral unmixing by
using an improved deep spectral convolution network (DSCN++) combined with
endmember uncertainty. DSCN++ is used to compute high-level representations
which are further modeled with Multinomial Mixture Model to estimate abundance
maps. In the reconstruction step, a new trainable uncertainty term based on a
nonlinear neural network model is introduced to provide robustness to endmember
uncertainty. For the optimization of the coefficients of the multinomial model
and the uncertainty term, Wasserstein Generative Adversarial Network (WGAN) is
exploited to improve stability and to capture uncertainty. Experiments are
performed on both real and synthetic datasets. The results validate that the
proposed method obtains state-of-the-art hyperspectral unmixing performance
particularly on the real datasets compared to the baseline techniques.
