Abstract
A generalized principal components transform (PCT) that maximizes the signal-to-noise ratio (SNR) and that tailors to the multiplicative speckle noise characteristics of polarimetric SAR images is developed. An implementation procedure that accurately estimates the signal and the noise covariance matrices is established. The properties of the eigenvalues and eigenvectors are investigated, revealing that the eigenvectors are not orthogonal, but the principal component images are statistically uncorrelated. Both amplitude (or intensity) and phase difference images are included for the PCT computation. The NASA/JPL polarimetric SAR imagery of P, L, and C bands and quadpolarizations is used for illustration. The capabilities of this principal components transformation in information compression and speckle reduction makes automated image segmentation and better human interpretation possible. >
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