Surface oxides characterization based on hyperspectral observations

Tarek Zenati

doi:10.1016/j.chemolab.2023.104879

2023-09-01

Tarek Zenati

Bruno Figliuzzi *

Seung-Ho Ham

DOI：10.1016/j.chemolab.2023.104879

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En 中文

During steel sheets manufacturing, the addition of alloying elements to the steel to improve its mechanical performance can cause the formation of layers of selective oxides at the surface. These layers can significantly impact the surface properties of the steel sheets, which makes their detection an essential task. In this article, we present a novel methodology based on hyperspectral measurements to detect the presence of surface layers of selective oxides on steel sheets. Hyperspectral sensors have the ability to measure the reflectance of the steel surface over a large spectral band of the electromagnetic spectrum, usually in the infrared range. However, due to their limited spatial resolution, the observed reflectance spectra usually mix the contribution of several elementary oxides. A first step toward surface characterization is therefore to identify the elementary oxides contributing to the hyperspectral observations, a task referred to as hyperspectral unmixing in the literature. In our case, a major difficulty is that the optical model describing the formation of the electromagnetic wave coming onto the sensors is strongly nonlinear. To alleviate this difficulty, we rely on an approach that formulates the hyperspectral unmixing task as a sparse regression problem. This approach works by pre-computing the optical reflectance associated to reference layers of heterogeneous oxides that can form at the surface to construct a dictionary of reflectance spectra and by seeking to recover the hyperspectral observations as a linear combination of a small number of elements taken from this dictionary. To perform the recovery, we propose and compare two approaches based on the path and group Lasso algorithms. We illustrate our methodology on numerical experiments, which demonstrate that our approach is able to estimate the presence of selective oxides with high recall and precision. These numerical experiments also highlight the main limitations of the proposed approach. We finally apply our estimation method to characterize the surface chemistry of experimental samples. Our characterization approach is then compared to observations conducted on the samples through transmission electron microscopy and XPS analysis to demonstrate its validity.

Keyword:

Hyperspectral unmixing

Sparse regression

Surface characterization