Surface temperature determination using long range thermal emission spectroscopy based on a first order scanning Fabry-Pérot interferometer

Abstract

Determination of the surface temperature of different materials based on thermographic imaging is a difficult task as the thermal emission spectrum is both temperature and emissivity dependent. Without prior knowledge of the emissivity of the object under investigation, it makes up a temperature-emissivity underdetermined system. This work demonstrates the possibility of recognizing specific materials from hyperspectral thermal images (HSTI) in the wavelength range from 8–14 µm. The hyperspectral images were acquired using a microbolometer sensor array in combination with a scanning 1st order Fabry-Pérot interferometer acting as a bandpass filter. A logistic regression model was used to successfully differentiate between polyimide tape, sapphire, borosilicate glass, fused silica, and alumina ceramic at temperatures as low as 34.0 ± 0.05 °C. Each material was recognized with true positive rates above 94% calculated from individual pixel spectra. The surface temperature of the samples was subsequently predicted using pre-fitted partial least squares (PLS) models, which predicted all surface temperature values with a common root mean square error (RMSE) of 1.10 °C and thereby outperforming conventional thermography. This approach paves the way for a practical solution to the underdetermined temperature-emissivity system.

A hyperspectral thermal imager based on a low order scanning Fabry-Pérot interferometer

“…It is presented how the SFPI is controlled as well as how the acquired data can be interpreted and used to material segregation. It has previously been shown how the same camera system can be used for material classification and subsequent temperature determination [10], while the focus of this work is on the instrument itself.…”

A residual dense comprehensively regulated convolutional neural network to identify spectral information for egg quality traceability

“…Traditional band processing methods of spectral information include independent component analysis, 13 linear discriminant analysis, 14 principal component analysis, 15 competitive adaptive reweighted sampling, 16 successive projection algorithm, 17 etc. After that, machine learning methods such as support vector machine, 18 decision trees, 19 and logistic regression 20 are used to classify spectral features. Traditional band processing methods require human adjustment of multiple parameters and extensive human intervention to obtain valuable features.…”