AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder
  and Regularization by Denoising
Min Zhao Jie Chen Nicolas Dobigeon
Spectral unmixing has been extensively studied with a variety of methods and
used in many applications. Recently, data-driven techniques with deep learning
methods have obtained great attention to spectral unmixing for its superior
learning ability to automatically learn the structure information. In
particular, autoencoder based architectures are elaborately designed to solve
blind unmixing and model complex nonlinear mixtures. Nevertheless, these
methods perform unmixing task as blackboxes and lack of interpretability. On
the other hand, conventional unmixing methods carefully design the regularizer
to add explicit information, in which algorithms such as plug-and-play (PnP)
strategies utilize off-the-shelf denoisers to plug powerful priors. In this
paper, we propose a generic unmixing framework to integrate the autoencoder
network with regularization by denoising (RED), named AE-RED. More specially,
we decompose the unmixing optimized problem into two subproblems. The first one
is solved using deep autoencoders to implicitly regularize the estimates and
model the mixture mechanism. The second one leverages the denoiser to bring in
the explicit information. In this way, both the characteristics of the deep
autoencoder based unmixing methods and priors provided by denoisers are merged
into our well-designed framework to enhance the unmixing performance.
Experiment results on both synthetic and real data sets show the superiority of
our proposed framework compared with state-of-the-art unmixing approaches.