RESUMO
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.
RESUMO
This study presents the first results of a new type of hyperspectral imager in the long-wave thermal radiation range from 8.0 to 14.0 µm which is simpler than readily available Fourier transform infrared spectroscopy-based imagers. Conventional thermography images the thermal radiation from hot objects, but an accurate determination of temperature is hampered by the often unknown emissivities of different materials present in the same image. This paper describes the setup and development of a hyperspectral thermal camera based on a low-order scanning Fabry-Pérot interferometer acting as a bandpass filter. A three-dimensional hyperspectral data cube (two spatial and one spectral dimension) was measured by imaging a high-emissivity carbon nanotube-coated surface (Vantablack), black painted aluminum, borosilicate glass, Kapton tape, and bare aluminum. A principal component analysis (PCA) of the hyperspectral thermal image clearly segregates the individual samples. The most distinguishable sample from the PCA is the borosilicate Petri dish of which the Si-O-Si bond in borosilicate glass was the most noticeable. Additionally, it was found that the relatively large 1024 × 768 × 70 data cube can be reduced to a much smaller cube of size 1024 × 768 × 5 containing 92% of the variance in the original dataset. The possibility of discriminating between the samples by their spectroscopic signature was tested using a logistic regression classifier. The model was fitted to a chosen set of principal components obtained from a PCA of the original hyperspectral data cube. The model was used to predict all pixels in the original data cube resulting in estimates with very high true positive rate (TPR). The highest TPR was obtained for borosilicate glass with a value of 99% correctly predicted pixels. The remaining TPRs were 94% for black painted aluminum, 81% for bare aluminum, 79% for Kapton tape, and 70% for Vantablack. A standard thermographic image was acquired of the same objects where it was found that the samples were mutually indistinguishable in this image. This shows that the hyperspectral thermal image contains sample characteristics which are material related and therefore outperforms standard thermography in the amount of information contained in an image.
