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. It displays popping noise
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: cm-1

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

11. RESULTS - # PCs = 10
Gaussian noise

12. RESULTS - # PCs = 175
Gaussian noise

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

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