Abstract

Determination of the surface temperature of different materials based on thermo- graphic 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 subse- quently 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.