Quantitative coating thickness determination using a coefficient-independent hyperspectral scattering model

Abstract

Hyperspectral imaging is a technique that enables the mapping of spectral signatures across a surface. It is most commonly used for surface chemical mapping in fields as diverse as satellite remote sensing, biomedical imaging and heritage science. Existing models, such as the Kubelka-Munk theory and the Lambert-Beer law also relate layer thickness with absorption, and in the case of the Kubelka-Munk theory scattering, however they are not able to fully describe the complex behavior of the light-layer interaction.

This paper describes a new approach for hyperspectral imaging, the mapping of coating surface thickness using a coefficient-independent scattering model. The approach taken in this paper is to model the absorption and scattering behavior using a developed coefficient-independent model, calibrated using reference sample thickness measurements performed with optical coherence tomography.

The results show that this new model, by considering the spectral variation that can be recorded by the hyperspectral imaging camera, is able to measure coatings of 250 μm thickness with an accuracy of 11 μm in a fast and repeatable way.

The new coefficient-independent scattering model presented can successfully measure the thickness of coatings from hyperspectral imaging data.