针对高光谱数据热红外温度和发射率反演为病态方程且易受大气下行辐射噪声干扰的问题, 提出了基于相关性和小波滤波相结合的高光谱热红外温度发射率分离方法, 即相关-小波法。在相关性方法的基础上, 引入小波降噪的思想, 生成一系列温度梯度, 在不同温度梯度下, 带入大气下行辐射计算得到的发射率曲线和不考虑大气下行辐射直接小波滤波得到的发射率曲线计算相关性, 取相关性最大时的温度为反演温度。同时在反演发射率时利用相关性计算不同尺度的小波信号所占的比例合成发射率曲线。模拟数据结果显示: 相关-小波法在温度梯度为0.01 K时, 温度反演平均误差为0.05 K, 并且相关-小波法在温度反演精度和发射率反演精度上都优于相关性方法和小波法。由此表明, 该算法可一定程度上抑制大气校正不准确引入的误差, 有效提高热红外温度和发射率的反演精度。

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.