  Data acquired from multi-channel sensors is a highly valuable asset to
interpret the environment for a variety of remote sensing applications.
However, low spatial resolution is a critical limitation for previous sensors
and the constituent materials of a scene can be mixed in different fractions
due to their spatial interactions. Spectral unmixing is a technique that allows
us to obtain the material spectral signatures and their fractions from
hyperspectral data. In this paper, we propose a novel endmember extraction and
hyperspectral unmixing scheme, so called \textit{EndNet}, that is based on a
two-staged autoencoder network. This well-known structure is completely
enhanced and restructured by introducing additional layers and a projection
metric (i.e., spectral angle distance (SAD) instead of inner product) to
achieve an optimum solution. Moreover, we present a novel loss function that is
composed of a Kullback-Leibler divergence term with SAD similarity and
additional penalty terms to improve the sparsity of the estimates. These
modifications enable us to set the common properties of endmembers such as
non-linearity and sparsity for autoencoder networks. Lastly, due to the
stochastic-gradient based approach, the method is scalable for large-scale data
and it can be accelerated on Graphical Processing Units (GPUs). To demonstrate
the superiority of our proposed method, we conduct extensive experiments on
several well-known datasets. The results confirm that the proposed method
considerably improves the performance compared to the state-of-the-art
techniques in literature.
