Spatial-Spectral Twin Autoencoders for Hyperspectral Unmixing Via Superpixel-Hypergraph-Augmented Feature Representation

Deep autoencoder networks’ flexibility and advantages in learning hidden features have made them popular in unmixing hyperspectral images recently. Based on specific network structures, the effective learning of spatial-spectral features for unmixing is always crucial. This paper presents a novel unmixing method using spatial-spectral twin autoencoders. One autoencoder generates coarse abundances with strong local spatial similarity for pixels in hyperedges defined in homogeneous superpixels. The other autoencoder with identical modules further adopts the coarse abundances to build a spatial resemblance constraint in the loss function to produce refined abundances. Experiments with simulated and real hyperspectral remote sensing data have confirmed the validity of the proposed method.