Spatial-Spectral Autoencoder Networks for Hyperspectral Unmixing

We present a spatial-spectral autoencoder (SSAE) for hyperspectral unmixing, including a net for endmember extraction (EENet) and a net for abundance estimation (AENet). The EENet exploits the spatial information in hyperspectral image by a "many to one" strategy, i.e., the abundance of a pixel is combined by the abundances of its adjacent pixels. The idea is based on the assumption: once an endmember is mixed in a pixel, it is mixed in the surrounding pixels with high probability. The strategy promotes a continuous and smooth spatial distribution of abundances, and it is more effective than the other methods for endmember extraction. Besides, to make full use of the rich spectral information and obtain more accurate abundances, we design an AENet, which applies the deep convolutional neural network to estimate the abundances with the endmembers acquired from the EENet. The experiments are conducted on two real datasets, which show the SSAE outperforms the state-of-the-art methods.