Instructional Leadership, emphasis: K-12 School Leadership (MEd)
Wires that are connected to a computer.

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.

Additional information

Patent number and inventor

17/353,077

Christopher Edwards, John Janiczek, Suren Jayasuriya, Gautam Dasarathy, and Philip Christensen.

Potential applications

Applications include remote sensing with capabilities in agriculture, minerology, landscape surveying, and more using hyperspectral imaging.

Benefits and advantages

These methods provide improved spectral unmixing performance including material identification and classification.

Case number and licensing status

2020-037

This invention is available for licensing.