  Over the past decades, enormous efforts have been made to improve the
performance of linear or nonlinear mixing models for hyperspectral unmixing,
yet their ability to simultaneously generalize various spectral variabilities
and extract physically meaningful endmembers still remains limited due to the
poor ability in data fitting and reconstruction and the sensitivity to various
spectral variabilities. Inspired by the powerful learning ability of deep
learning, we attempt to develop a general deep learning approach for
hyperspectral unmixing, by fully considering the properties of endmembers
extracted from the hyperspectral imagery, called endmember-guided unmixing
network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a
two-stream Siamese deep network, which learns an additional network from the
pure or nearly-pure endmembers to correct the weights of another unmixing
network by sharing network parameters and adding spectrally meaningful
constraints (e.g., non-negativity and sum-to-one) towards a more accurate and
interpretable unmixing solution. Furthermore, the resulting general framework
is not only limited to pixel-wise spectral unmixing but also applicable to
spatial information modeling with convolutional operators for spatial-spectral
unmixing. Experimental results conducted on three different datasets with the
ground-truth of abundance maps corresponding to each material demonstrate the
effectiveness and superiority of the EGU-Net over state-of-the-art unmixing
algorithms. The codes will be available from the website:
https://github.com/danfenghong/IEEE_TNNLS_EGU-Net.
