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Multibaseline InSAR terrain elevation estimation: a dynamic programming approach

When estimating terrain elevation via interferometric synthetic aperture radar (InSAR), phase unwrapping procedures have difficulty in dealing with rough regions or large noise. Multiple baseline is used to reduce or avoid this problem. Conventional maximum likelihood (ML) methods reconstruct terrain heights in a pointwise fashion, which does not utilize the smooth characteristics of natural terrain. We propose a new algorithm taking smoothness into account. The new approach tackles the problem in a Bayesian framework. Instead of using ML estimation, we use maximum a posteriori (MAP) estimation, where the likelihood function is defined as in the ML method and the prior is defined as a first-order Gaussian Markov random field. This MAP estimation makes the algorithm more robust to noise, and at the same time, more accurate in reconstructing rough regions. A form of 2-D dynamic programming is used to implement the MAP estimation efficiently. The new algorithm has the advantage over the ML methods in that none of the baselines must be chosen so small as to avoid phase wrapping. Specifically, both baselines can be large so that the noise in the reconstructed height can be low. The new algorithm is shown to be able to achieve lower noise than the conventional ML and least-squares methods.