Variational Autoencoders for Hyperspectral Unmixing with Endmember Variability

Abstract

Spectral signatures are usually affected by variations in environmental conditions. The spectral variability is thus one of the most important and challenging problems to be addressed in hyperspectral unmixing. Generally, it is a non-trivial task to model the endmember variability, and existing spectral unmixing methods that address the spectral variability have different limitations. This paper presents a variational autoencoder (VAE) framework for hyperspectral unmixing accounting for the endmember variability. The endmembers are generated using the posterior distributions of the latent variables to describe their variability in the image. Compared with other existing distribution based methods, the proposed method is able to fit an arbitrary distribution of endmembers for each material through the representation capacity of deep neural networks. Evaluated with both synthetic and real datasets, the proposed method shows superior unmixing results compared with other state-of-the-art unmixing methods.