InSAR phase unwrapping: a Bayesian approach
The paper proposes a Bayesian approach to absolute phase (not simply modulo-2/spl pi/) estimation in interferometric aperture radar (InSAR). The observation density is 2/spl pi/-periodic and accounts for the interferometric pair decorrelation and the system noise; the a priori. probability of the absolute phase is modeled by a compound Gauss Markov random field (CGMRF). To compute the absolute phase estimate we propose an iterative scheme aiming at the computation of the maximum a posteriori probability (MAP) estimate. Each iteration embodies a discrete optimization step (Z-step), implemented by network programming techniques, and an iterative conditional modes (ICM) step (/spl pi/-step). According to the terms Z-step and /spl pi/-step, we term our algorithm Z/spl pi/M, where the letter M stands for maximization. Experimental results, comparing the proposed algorithm with classical approaches, illustrate the effectiveness of the Z/spl pi/M algorithm.