Dr. Yanni Qu Profile

Dr. Yanni Qu

at National Oceanic and Atmospheric Administration

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Proceedings Article | 16 June 2003 Paper

Noise estimating and filtering of hyperspectral infrared radiance

Yanni Qu, Mitchell Goldberg

Proceedings Volume 4897, (2003) https://doi.org/10.1117/12.467645

KEYWORDS: Interference (communication), Signal to noise ratio, Principal component analysis, Infrared radiation, Computer simulations, Error analysis, Clouds, Calibration, Satellites, Device simulation

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Atmospheric InfraRed Sounder (AIRS) is a hyper-spectral infrared instrument on the EOS-Aqua satellite. Principal Component Analysis (PCA) of realistic simulations of AIRS radiances, which includes the effects of variable clouds and surface parameters, can be used to estimate and filter AIRS instrumental noise. The PCA uses noise scaled radiance, i.e. radiance divided by noise (R/N). Since the square root of the eigenvalues is equivalent to the standard deviation of the principal component score of the dependent ensemble, the square root of the eigenvalues in the R/N domain can be interpreted as a signal to the noise ratio for the new principal components (PC) or say "abstract channels". New PCs are arranged from the largest to the smallest eigenvalues. Once the R/N ratio is below unity, the signal has less contribution than noise for that PC and all the remaining PCs. This physical meaningful fact can be a criterion for radiance noise filtering. Using the linearity of PCA to the pure signal S plus ideal noise N in the R/N domain, PCA(N/N) becomes PCA(I). I is the identity matrix and hence eigenvalues of I are all equal. If reconstructing I, only k/m information can be recovered by k eigenvectors, where k = 1 .... m eigenvectors. Thus, it is easy to know how much noise merged into the reconstructed radiance and how many eigenvectors are needed for filtering noise when performing PCA in R/N domain. The number of eigenvectors to be used for noise estimating is around 60 in all sky conditions. 60 eigenvectors can filter over 97% of noise. Only 3% noise remains in the reconstructed data. Signal information can be recovered with accuracy in noise level. In addition, there is less than 5% error using PCA to estimate noise. Preliminary results from the real AIRS observation are discussed briefly.

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