Abstract：The noise reduction with observed high resolution infrared radiance is crucial to improve the accuracy and stability of the retrieval of thermodynamic profiles. When applying the principal component analysis noise filter algorithm to the observed radiance, the optimal number k of principal components that used in the algorithm was mostly calculated with the statistical and empirical method. The percent cumulative variance method is one of the statistical methods that have been commonly used to calculate k, however, the threshold of the percent cumulative variance was determined subjectively and arbitrarily, which limits the application of this method. While the empirical method need the real-time Noise-Equivalent Spectral Radiance (NESR) to normalize non uniform noise in the observed data, but the real-time NESR needs the raw data of complex spectrum which is not easy to obtain in most cases. Aiming at the solving the problems above, a PCA noise filter based on the Improved PCV algorithm is proposed, of which the threshold is determined by iteratively calculating the difference between the simulated and reconstructed spectrum using different principal components, whereby k is determined such that the PCV is larger than the threshold. The new method solves the problem of arbitrary of the determination of k, and at the same time it doesn’t need the real-time NESR to normalize the observed radiance. First, the impact of normalization on the noise reduction is analyzed using physical retrieval of temperature profiles; the result shows that the impact is very small, which less than the impact of calculation error of k is caused by normalization on the retrieval of temperature profiles. Then, the noise reduction of the representative radiance data which covers four quarters of 2011 shows that, the RMSE of the retrieved temperature profile using the Improved PCV method is improved by 0.1 K compared to the factor indicator function method when the real-time NESR is not available, and it is almost the same with the latter when the normalization is done. Under the condition that the NESR is not available, the method proposed in this article could objectively and reasonably reduce the noise level of the ground-based high resolution infrared radiance.
Key words：Noise reduction of hyper-spectral;PCV method;Normalization;Newtonian nonlinear iteration retrieval technique
黄 威，高太长*，刘 磊，李书磊 . 一种基于改进累积方差百分比的红外高光谱数据降噪方法 [J]. 光谱学与光谱分析, 2016, 36(11): 3625-3629.
HUANG Wei, GAO Tai-chang*, LIU Lei, LI Shu-lei . Research on the Noise Reduction with Hyper-Resolution Infrared Spectrum Based on Improved PCV Method. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(11): 3625-3629.
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