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