Sushi: Learning-Based Hyperspectral Image Unmixing with Spectral Variabilities.

Hyperspectral images (HI) are cubes of data with two spatial dimensions and a third spectral dimension. In this paper, we present SUSHI (Semi Blind Unmixing with Sparsity for Hyperspectral Images), an algorithm for unmixing HI with spectral variability that can be described by a physical model, which need not be analytical. To obtain a differentiable, parametric surrogate spectral model, we use a network called an Interpolatory Auto-Encoder (IAE), and plug it in a state-of-the-art optimizing architecture for solving regularized inverse model. We apply a constraint of spatial regularization on the latent parameters, to account for correlations between pixels instead of treating them individually. We test SUSHI on a toy-model inspired by supernova remnants as seen by the X-ray telescope Chandra. Our results are a net improvement on those obtained with the classic method which is usually applied in the astrophysics community.