  We present a method for hyperspectral pixel {\it unmixing}. The proposed
method assumes that (1) {\it abundances} can be encoded as Dirichlet
distributions and (2) spectra of {\it endmembers} can be represented as
multivariate Normal distributions. The method solves the problem of abundance
estimation and endmember extraction within a variational autoencoder setting
where a Dirichlet bottleneck layer models the abundances, and the decoder
performs endmember extraction. The proposed method can also leverage transfer
learning paradigm, where the model is only trained on synthetic data containing
pixels that are linear combinations of one or more endmembers of interest. In
this case, we retrieve endmembers (spectra) from the United States Geological
Survey Spectral Library. The model thus trained can be subsequently used to
perform pixel unmixing on "real data" that contains a subset of the endmembers
used to generated the synthetic data. The model achieves state-of-the-art
results on several benchmarks: Cuprite, Urban Hydice and Samson. We also
present new synthetic dataset, OnTech-HSI-Syn-21, that can be used to study
hyperspectral pixel unmixing methods. We showcase the transfer learning
capabilities of the proposed model on Cuprite and OnTech-HSI-Syn-21 datasets.
In summary, the proposed method can be applied for pixel unmixing a variety of
domains, including agriculture, forestry, mineralogy, analysis of materials,
healthcare, etc. Additionally, the proposed method eschews the need for
labelled data for training by leveraging the transfer learning paradigm, where
the model is trained on synthetic data generated using the endmembers present
in the "real" data.
