  In this paper, we propose a novel hyperspectral unmixing technique based on
deep spectral convolution networks (DSCN). Particularly, three important
contributions are presented throughout this paper. First, fully-connected
linear operation is replaced with spectral convolutions to extract local
spectral characteristics from hyperspectral signatures with a deeper network
architecture. Second, instead of batch normalization, we propose a spectral
normalization layer which improves the selectivity of filters by normalizing
their spectral responses. Third, we introduce two fusion configurations that
produce ideal abundance maps by using the abstract representations computed
from previous layers. In experiments, we use two real datasets to evaluate the
performance of our method with other baseline techniques. The experimental
results validate that the proposed method outperforms baselines based on Root
Mean Square Error (RMSE).
