Kapitel/Beitrag aus dem Buch: Beyerer J. & Längle T. 2021. OCM 2021 – 5th International Conference on Optical Characterization of Materials, March 17th – 18th, 2021, Karlsruhe, Germany : Conference Proceedings.
A stochastic method how artificial training data for
spectral unmixing can be generated from real pure spectra is presented.
Since the pure spectra are modelled as Gaussian random
vectors, spectral variability is also considered. These training
data can in turn be used to train an artificial neural network for
spectral unmixing. Non-negativity and sum-to-one constraints
are enforced by the network architecture. The approach is evaluated
using real mixed spectra and achieves promising results
with the used datasets.
Anastasiadis J. & Heizmann M. 2021. Generation of artificial training data forspectral unmixing by modelling spectralvariability using Gaussian random variables. In: Beyerer J. & Längle T (eds.), OCM 2021 – 5th International Conference on Optical Characterization of Materials, March 17th – 18th, 2021, Karlsruhe, Germany : Conference Proceedings. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000128686-12
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Veröffentlicht am 17. März 2021