A novel method of thermal infrared temperature emissivity separation based on correlation and wavelet filtering was proposed to alleviate the ill-conditioned equation problem of thermal infrared temperature and emissivity inversions of hyperspectral data. The basis of the utilized correlation method was the idea that wavelet denoising could be introduced to suppress the error caused by inaccurate atmospheric correction to certain extents, effectively improving the inversion precision of thermal infrared temperature and emissivity. The core goal of the algorithm was to calculate the correlation between the emissivity curves generated by both the atmospheric downward radiation calculation and the wavelet filtering; the temperature with the highest correlation was the inversion temperature. At the same time, correlation was used to calculate the proportion of different scale wavelet signals in the inversion of their emissivity curves. The simulation results show that the correlation wavelet method has an average error of 0.05 K in temperature calculations when the temperature gradient is 0.01 K. In addition, the combined correlation wavelet method is superior to both the correlation and wavelet methods with regards to temperature inversion accuracy and emissivity inversion precision. It is shown that the developed algorithm can restrain the error caused by inaccurate atmospheric correction to a certain extent as well as effectively improve the inversion precision of thermal infrared temperature and emissivity.