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.

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

“…It was also later extended in [140] to consider SU integrating both reflectance and derivative spectra. Band weights based on the instability index were also used to prioritize the more stable spectral bands when designing spectral filters robust to spectral variability, which are low-complexity alternatives that approximate the solution of the SU problem as a direct application of a single linear transformation [141].…”

Erzeugung künstlicher Datensätze zum Training konvolutionaler neuronaler Netze für die spektrale Entmischung

“…Das am häufigsten verwendete Mischmodell ist das lineare Mischmodell (LMM), welches für viele Anwendun-gen eine gute Näherung darstellt [4,[16][17][18][19]:…”

Ortsaufgelöste spektrale Entmischung mit Hilfe von konvolutionalen neuronalen Netzen / Spatially resolved spectral unmixing using convolutional neural networks

“…Sind die Spektren der Reinstoffe bekannt, wird von überwachter spektraler Entmischung gesprochen. Die meisten Entmischungsverfahren basieren auf einem Mischmodell, wobei das lineare Mischmodell (LMM) am häufigsten verwendet wird, da es für viele Anwendungen eine gute Näherung darstellt [3,[8][9][10][11]:…”