Considering spectral variability for optical material abundance estimation

Abstract Hyperspectral images include information enabling the determination of material abundances. Due to the fact that the acquisition of hyperspectral images is time consuming and the processing of these images is computationally costly, we propose an optical approach using spectral filters to retrieve the material abundances. The application of a spectral filter leads to an intensity image encoding estimates for the abundances of a specific material. The acquisition and processing of hyperspectral images becomes superfluous. However, the determination of spectral filters offers a large degree of freedom. In this work, we focus on methods for designing spectral filters incorporating spectral variability. Particularly, we account for reducing the negative effects of spectral variability on the accuracy of estimates for material abundances. According to experimental evaluations, we conclude that including spectral variability into the calculation of spectral filters leads to more accurate abundance estimates when mixed spectra of the considered material mixtures sufficiently fulfill the linear mixing model.