Generalized inverse matrix-graphic deep learning algorithm for multispectral pyrometer temperature inversion.

Abstract

The multispectral radiometric temperature measurement technique is affected by the unknown emissivity, and there is no multispectral radiometric temperature inversion algorithm applicable to any scene or target. To address the above problems, this paper converts the multispectral radiometric temperature inversion problem into an image recognition problem containing the temperature information to be measured, and proposes a graphical multispectral radiometric temperature adaptive inversion algorithm. In this paper, we use the difference between spectral channels to convert the one-dimensional radiation data into a two-dimensional radiation map; use the generalized inverse to obtain the spectral emissivity distribution features, fuse them with the two-dimensional radiation map, and use an improved deep learning network to achieve adaptive temperature inversion. It is experimentally verified that the algorithm proposed in this paper can achieve simultaneous inversion of temperature and emissivity for any scene or target with sufficient data set.