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

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 mu m. The hyperspectral images were acquired using a microbolometer sensor array in combination with a scanning 1st order Fabry-Perot 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 degrees 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 degrees C and thereby outperforming conventional thermography. This approach paves the way for a practical solution to the underdetermined temperature-emissivity system. (C) 2022 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This study demonstrates the possibility of recognizing specific materials from hyperspectral thermal images and successfully distinguishes multiple materials using a logistic regression model. The surface temperature is predicted using a partial least squares (PLS) model, which outperforms conventional thermography with a smaller root mean square error.