Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model

• URL: http://arxiv.org/abs/2007.05996v1
• Date: Sun, 12 Jul 2020 14:16:35 GMT
• Title: Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
• Authors: John Janiczek, Parth Thaker, Gautam Dasarathy, Christopher S. Edwards, Philip Christensen, Suren Jayasuriya
• Abstract summary: In this paper, spectral variation is considered from a physics-based approach and incorporated into an end-to-end spectral unmixing algorithm. A technique for inverse rendering using a convolutional neural network 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.
• Score: 9.96234892716562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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