  Autoencoder (AEC) networks have recently emerged as a promising approach to
perform unsupervised hyperspectral unmixing (HU) by associating the latent
representations with the abundances, the decoder with the mixing model and the
encoder with its inverse. AECs are especially appealing for nonlinear HU since
they lead to unsupervised and model-free algorithms. However, existing
approaches fail to explore the fact that the encoder should invert the mixing
process, which might reduce their robustness. In this paper, we propose a
model-based AEC for nonlinear HU by considering the mixing model a nonlinear
fluctuation over a linear mixture. Differently from previous works, we show
that this restriction naturally imposes a particular structure to both the
encoder and to the decoder networks. This introduces prior information in the
AEC without reducing the flexibility of the mixing model. Simulations with
synthetic and real data indicate that the proposed strategy improves nonlinear
HU.
