  In the remote sensing context spectral unmixing is a technique to decompose a
mixed pixel into two fundamental representatives: endmembers and abundances. In
this paper, a novel architecture is proposed to perform blind unmixing on
hyperspectral images. The proposed architecture consists of convolutional
layers followed by an autoencoder. The encoder transforms the feature space
produced through convolutional layers to a latent space representation. Then,
from these latent characteristics the decoder reconstructs the roll-out image
of the monochrome image which is at the input of the architecture; and each
single-band image is fed sequentially. Experimental results on real
hyperspectral data concludes that the proposed algorithm outperforms existing
unmixing methods at abundance estimation and generates competitive results for
endmember extraction with RMSE and SAD as the metrics, respectively.
