Terrain classification in polarimetric SAR using wavelet packets

POL-SAR data acquired from the two 1994 flights of the SIR-C/X-SAR platform has illustrated the variability of measurements due to seasonal, spectral, and angular changes. Consequently statistical techniques for terrain classification make robust, unsupervised classification problematic. We present an algorithm for classifying terrain that accounts for variability in terrain signatures by deriving a single representative process for each terrain from a family of stochastic scattering models. A best-basis search through a wavelet packet tree, using the Bhattacharyya coefficient as a cost measure, determines the optimal unitary basis of eigenvectors for the representative process and offers a scale-based interpretation of the scattering phenomena. The associated eigenvalues and means are determined through iterative algorithms. The technique is illustrated with a simple example