  This study proposes a novel framework for spectral unmixing by using 1D
convolution kernels and spectral uncertainty. High-level representations are
computed from data, and they are further modeled with the Multinomial Mixture
Model to estimate fractions under severe spectral uncertainty. Furthermore, a
new trainable uncertainty term based on a nonlinear neural network model is
introduced in the reconstruction step. All uncertainty models are optimized by
Wasserstein Generative Adversarial Network (WGAN) to improve stability and
capture uncertainty. Experiments are performed on both real and synthetic
datasets. The results validate that the proposed method obtains
state-of-the-art performance, especially for the real datasets compared to the
baselines. Project page at: https://github.com/savasozkan/dscn.
