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: • random • gaussian • time and space decorrelation

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.

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

FFT FILTER RESULTS This filter doesn’t work!!

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.

UNFILTERED DATA 101 AIRS SPECTRA

STANDARD NOISE RANGE CONSIDERED: 700-1100 cm-1

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

RESULTS - # PCs = 10 Gaussian noise

RESULTS - # PCs = 175 Gaussian noise

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

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

NOISE FILTER AND PC FILTERING