PRINCIPAL COMPONENTS TRANSFORMATION OF MULTIFREQUENCY POLARIMETRIC SAR IMAGERY

A generalized principal components transform (PCT) is developed which maximizes the signal-to-noise ratio (SNR) and which tailors to the multiplicative speckle noise characteristics of polarimetric SAR images. An implementation procedure which 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 capability of this principal components transformation in information compression and speckle reduction enables automated image segmentation and better human interpretation.