  Hyperspectral unmixing is an important remote sensing task with applications
including material identification and analysis. Characteristic spectral
features make many pure materials identifiable from their visible-to-infrared
spectra, but quantifying their presence within a mixture is a challenging task
due to nonlinearities and factors of variation. In this paper, spectral
variation is considered from a physics-based approach and incorporated into an
end-to-end spectral unmixing algorithm via differentiable programming. The
dispersion model is introduced to simulate realistic spectral variation, and an
efficient method to fit the parameters is presented. Then, this dispersion
model is utilized as a generative model within an analysis-by-synthesis
spectral unmixing algorithm. Further, a technique for inverse rendering using a
convolutional neural network to predict parameters of the generative model is
introduced to enhance performance and speed when training data is available.
Results achieve state-of-the-art on both infrared and visible-to-near-infrared
(VNIR) datasets, and show promise for the synergy between physics-based models
and deep learning in hyperspectral unmixing in the future.
