  Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading
approaches to perform spectral unmixing (SU) considering variability of the
endmembers (EMs). It represents each EM in the image using libraries of
spectral signatures acquired a priori. However, existing spectral libraries are
often small and unable to properly capture the variability of each EM in
practical scenes, which compromises the performance of MESMA. In this paper, we
propose a library augmentation strategy to increase the diversity of existing
spectral libraries, thus improving their ability to represent the materials in
real images. First, we leverage the power of deep generative models to learn
the statistical distribution of the EMs based on the spectral signatures
available in the existing libraries. Afterwards, new samples can be drawn from
the learned EM distributions and used to augment the spectral libraries,
improving the overall quality of the SU process. Experimental results using
synthetic and real data attest the superior performance of the proposed method
even under library mismatch conditions.
