Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder

Hyperspectral pixel intensities result from a mixing of reflectances from several materials. This paper develops a method of hyperspectral pixel {\it unmixing} that aims to recover the "pure" spectral signal of each material (hereafter referred to as {\it endmembers}) together with the mixing ratios ({\it abundances}) given the spectrum of a single pixel. The unmixing problem is particularly relevant in the case of low-resolution hyperspectral images captured in a remote sensing setting, where individual pixels can cover large regions of the scene. Under the assumptions that (1) a multivariate Normal distribution can represent the spectra of an endmember and (2) a Dirichlet distribution can encode abundances of different endmembers, we develop a Latent Dirichlet Variational Autoencoder for hyperspectral pixel unmixing. Our approach achieves state-of-the-art results on standard benchmarks and on synthetic data generated using United States Geological Survey spectral library.