Modeling infrared radiance and calculating spectral emissivity based on RBF network

A method of modeling infrared radiance based on RBF neural network was built, then the target spectral emissivity was estimated. When measuring the infrared radiation characteristics of the target surface in the 3-14 m band by FTIR spectrometer, the infrared radiance will be absorbed by carbon dioxide, water vapor, etc, and affected by some stray radiation. In this paper, the effective learning samples were firstly selected combined with the theory of infrared transmission. Then the samples based on the RBF network were fully learned, and a target infrared radiance model was built. And this model was used to estimate the radiance in the band of atmospheric absorption and stray radiation. A more complete target spectral emissivity curve was finally calculated. Compared the calculating results of blackbody with theoretical emissivity, the maximum relative error is 1.5%. The verification of temperature measurement also shows that the RBF neural networks can be built efficiently to estimate target spectral emissivity.