Graphical multispectral radiation temperature inversion algorithm based on deep learning

doi:10.1364/OL.487425

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Graphical multispectral radiation temperature inversion algorithm based on deep learning

2023-04-13

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DOI：10.1364/OL.487425

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Abstract

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Neural networks are the most promising tool to solve the problem that an assumed emissivity model is needed in the field of multispectral radiometric temperature measure-ment. Existing neural network multispectral radiometric temperature measurement algorithms have been investigat-ing the problems of network selection, network porting, and parameter optimization. The inversion accuracy and adaptability of the algorithms have been unsatisfactory. In view of the great success of deep learning in the field of image processing, this Letter proposes the idea of convert-ing one-dimensional multispectral radiometric temperature data into two-dimensional image data for data processing to improve the accuracy and adaptability of multispectral radiometric temperature measurement by deep learning algorithms. Simulation and experimental validation are car-ried out. In the simulation, the error is less than 0.71% without noise and 1.80% with 5% random noise, which improves the accuracy by more than 1.55% and 2.66% com-pared with the classical BP (backpropagation) algorithm, and 0.94% and 0.96% compared with the GIM-LSTM (generalized inverse matrix-long short-term memory) algo-rithm. In the experiment, the error is less than 0.83%. This indicates that the method has high research value and is expected to lead multispectral radiometric temperature measurement technology to a new level. (c) 2023 Optica Pub-lishing Group

Keywords:

PYROMETRY