Abstract

Reference materials are used in diffuse reflectance imaging for transforming the digitized camera signal into reflectance and absorbance units for subsequent interpretation. Traditional white and dark reference signals are generally used for calculating reflectance or absorbance, but these can be supplemented with additional reflectance targets to improve the accuracy of reflectance transformations. In this work we provide an overview of hyperspectral image regression and assess the effects of reflectance calibration on image interpretation using partial least squares regression. Linear and quadratic reflectance transformations based on additional reflectance targets decrease average measurement errors and make it easier to estimate model pseudorank during image regression. The lowest measurement and prediction errors were obtained with the column and wavelength specific quadratic transformations which retained the spatial information provided by the line-scanning instrument and reduced errors in the predicted concentration maps. (C) 2020 Elsevier B.V. All rights reserved.

Keywords

Hyperspectral imaging; Reflectance calibration; Prediction; Partial least squares; Textile analysis; Pseudorank

Published in

Analytica Chimica Acta
2020, Volume: 1105, pages: 56-63
Publisher: ELSEVIER

SLU Authors

• Mäkelä, Mikko

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    
  • Aalto University

• Geladi, Paul

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing
Other Chemical Engineering


Publication identifier

DOI: https://doi.org/10.1016/j.aca.2020.01.019

Permanent link to this page (URI)

https://res.slu.se/id/publ/104965