Temperature distribution inversion in infrared multispectral imaging based on ensemble network

Temperature distribution can be acquired through non-contact temperature measurement using multispectral imaging. However, the challenge lies in radiometric temperature inversion owing to the unknown emissivity. Despite the promising results demonstrated by traditional algorithms and neural networks, enhancing the precision and reliability of temperature inversion remains a challenge. To tackle these challenges, in this work, we propose the use of ensemble learning for temperature distribution inversion in infrared multispectral imaging. The network comprises a base-learner and a meta-learner, trained to establish the nonlinear relationship between temperature and multispectral distribution measurements. Moreover, the network architecture exhibits high robustness against noise arising in the testing environment. Simulations and real experiments on multispectral imaging measurements illustrate that ensemble learning can be a potent tool for multispectral imaging radiation temperature distribution measurement, achieving superior inversion performance compared to other neural networks. The reproducible code will be available at https://github.com/shuowenyang/Temperature-Inversion.