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.

It displays popping noise 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 =10 # PCs =175 X 5

RESULTS - # PCs = 10 Gaussian noise

RESULTS - # PCs = 175 Gaussian noise

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

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