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NOISE FILTER AND PC FILTERING

NOISE FILTER AND PC FILTERING. UFO GROUP. LUISA MARIA GRAZIA SILVIA VITO. OUTLINE. Presence of noise. FFT filter. PCA filter. Conclusions. PRESENCE OF NOISE. Noise affecting data is due to several factors. Correlated noise (calibration, instrumental vibration) Detectors noise:

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NOISE FILTER AND PC FILTERING

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  1. NOISE FILTER AND PC FILTERING UFO GROUP LUISA MARIA GRAZIA SILVIA VITO

  2. OUTLINE • Presence of noise. • FFT filter. • PCA filter. • Conclusions.

  3. PRESENCE OF NOISE • Noise affecting data is due to several factors. • Correlated noise (calibration, instrumental vibration) • Detectors noise: • random • gaussian • time and space decorrelation

  4. FFT FILTER ON AIRS DATA • Selection of two neighbor wavelengths with close Radiances. • Difference of the selected wavelengths for the same image. • FFT: noise corresponds to higher frequencies.

  5. R(l1)-R(l2) It displays popping noise FFT Frequencies

  6. FFT FILTER RESULTS This filter doesn’t work!!

  7. PCA – Principal Component Analysis • It allows to reduce the dimensionality of the problem. • It finds the Principal components (the eigenvectors of the observation covariance matrix). • Most of atmospheric of the observed variance is in the first PCs. • Right number of PCs allows for noise reduction and information preservation.

  8. UNFILTERED DATA 101 AIRS SPECTRA

  9. STANDARD NOISE RANGE CONSIDERED: 700-1100 cm-1

  10. FILTER SENSITIVITY TO THE NUMBER OF PCs MEAN OF NORMALIZED DIFFERENCE BETWEEN ESTIMATED AND STANDARD NOISE # PCs =175 # PCs =10 X 5

  11. RESULTS - # PCs = 10 Gaussian noise

  12. RESULTS - # PCs = 175 Gaussian noise

  13. FILTERED SPECTRA UNFILTERED SPECTRA # PCs = 10 # PCs = 175

  14. …that’s all folks!! THANKS FOR YOUR ATTENTION!!

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