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Interferometric synthetic aperture radar (SAR) (InSAR) systems allow 3-D reconstruction of observed scene. In this paper, an innovative approach for phase unwrapping and digital elevation model (DEM) generation using multichannel InSAR data is presented. The proposed algorithm, exploiting both the amplitude and phase of the available complex data, is able to unwrap and simultaneously regularize the observed data. In particular, the exploitation of amplitude data within the unwrapping chain helps in preserving sharp discontinuities typical of urban areas. As a result, the technique provides accurate DEM reconstructions. For this aim, a Markovian approach, together with a new graph-cut-based optimization algorithm, has been considered. The method has been developed specifically to work in urban areas with very high resolution InSAR image stacks, being able to automatically compensate possible phase offsets. Results on both simulated and real case studies are reported, showing the effectiveness of the metho

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a Markovian approach to solve the PU problem based on a new a priori model, the total variation, and graph-cut-based optimization algorithms. The proposed method turns out to be fast, simple, and robust. Moreover, compared with other approaches, the proposed algorithm is able to unwrap and restore the solution at the same time, without any additional filtering. A set of experimental results on both simulated and real data illustrates the effectiveness of our approach