Hyperspectral near infrared image calibration and regression

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.

Keywords

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

DOI

10.1016/j.aca.2020.01.019

Citation

Mäkelä, M, Geladi, P, Rissanen, M, Rautkari, L & Dahl, O 2020, ' Hyperspectral near infrared image calibration and regression ', Analytica Chimica Acta, vol. 1105, pp. 56-63 . https://doi.org/10.1016/j.aca.2020.01.019

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https://urn.fi/URN:NBN:fi:aalto-202003062447

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[article-cris] Kemian tekniikan korkeakoulu / CHEM

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Hyperspectral near infrared image calibration and regression

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