Systems and Methods for Differentiable Programming for Hyperspectral Unmixing

Description

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. This method provides an end-to-end spectral unmixing algorithm via differentiable programming. Excellent results are achieved on both infrared and visible-to-near-infrared (VNIR) datasets as compared to baselines and show promise for the synergy between physics-based models and deep learning in hyperspectral unmixing in the future.

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