Infrared Multispectral Radiation Temperature Measurement Based on PCA-ELM

Abstract

In the case of unknown target emissivity, an infrared multispectral radiation temperature measurement method based on principal component analysis (PCA) and extreme learning machine (ELM) is established. The nonlinear mathematic model of target temperature and radiance spectrum is analyzed to find a set of initial input vectors, which include sufficient information to estimate temperature. The PCA method is used to extract the independent principle components in input vectors. This method can also reduce the input dimension for neural network. Based on ELM network, the sample data is sufficiently learned to build the target infrared temperature measurement model by PCA-ELM. The effectiveness of the proposed method is verified by using the blackbody and the coating material with unknown emissivity as test target sources.

Keywords

• principal component analysis (pca)
• extreme learning machine (elm)
• multispectral thermometry
• radiance