Abstract :
SAR interferograms are affected by a strong noise
component which often prevents correct phase unwrapping and
always impairs the phase reconstruction accuracy. To obtain
satisfactory performance, most filtering techniques exploit prior
information by means of ad hoc, empirical strategies. In this
paper, we recast phase filtering as a Bayesian estimation problem
in which the image prior is modeled as a suitable Markov random
field, and the filtered phase field is the configuration with maximum
a posteriori probability. Assuming the image to be residue
free and generally smooth, a two-component MRF model is
adopted, where the first component penalizes residues, while the
second one penalizes discontinuities. Constrained aimulated annealing
is then used to find the optimal solution. The experimental
analysis shows that, by gradually adjusting the MRF parameters,
the algorithm filters out most of the high-frequency noise and,
in the limit, eliminates all residues, allowing for a trivial phase
unwrapping. Given a limited processing time, the algorithm is still
able to eliminate most residues, paving the way for the successful
use of any subsequent phase unwrapping technique.
