Sushi: Learning-Based Hyperspectral Image Unmixing with Spectral Variabilities

• Published in: 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) pp. 1 - 5
• Main Authors: Lascar, J., Bobin, J., Acero, F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 31.10.2023
• Subjects: Analytical models; Astrophysics; Correlation; Hyperspectral images; Hyperspectral imaging; inverse problems; machine learning; plug and play; regularisation; Signal processing algorithms; source separation; Spatial databases; X-ray telescopes
• ISSN: 2158-6276
• DOI: 10.1109/WHISPERS61460.2023.10431141

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.