High-Temperature Measurement Method Based on Ensemble Learning with Combined Dual-Color and Tri-Color Colorimetric Thermometry*

Rapid and accurate measurement of the temperature field is crucial in temperature control systems. However, conventional CCD-based thermometry treats the choice between dual-channel and tri-channel information as an either-or decision. To combine the advantages of both methods, this paper proposes an ensemble learning-based two-color and three-color joint thermometry method. The entire process, from spectral radiation reception to pixel intensity generation, was analyzed to understand its impact on selecting the optimal thermometry formula. By integrating channel reliability, noise parameters, and coefficients of variation in both spatial and colorimetric domains, a random forest model was used to predict the optimal thermometry formula. Experimental results demonstrate that the proposed method effectively selects the optimal thermometry method for each thermographic image.