A Novel Algorithm for Point Coherence Estimation in SAR Interferometry

Synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor from satellite very large areas and detect motions of the ground surface (typically due to subsidence, landslides, earthquakes, and volcanic phenomena) with millimetric precision and sub-metric spatial detail (making it possible to distinguish different parts of buildings or infrastructures). A key step of this technology is the identification of points (or clusters of points) providing a coherent backscattering over time. These points typically correspond to man-made structures, rocks, or bare soil, and can be called persistent scatterers (PSs) regardless of whether the dominant diffusion mechanism is point-like or distributed. In this work, we propose a novel algorithm, which we will call point coherence estimation (PCE), to evaluate in a clean and simple way (without the need for articulated procedures and critical assumptions or approximations) the interferometric coherence of each single point in an interferometric image series. The method exploits the well-accepted assumptions that large phase artefacts such as atmospheric delays or orbital effects are almost identical between points within tens or hundreds of meters, whereas phase noise (e.g., thermal noise and time, spectral, geometric decorrelation noises) is relatively small w.r.t. a phase cycle and has statistically independent realizations in different points (possibly excluding adjacent pixels if the images are oversampled). Based only on these assumptions, it is possible to write an overdetermined system of linear equations to evaluate the coherence of each single point reliably and consistently from the coherences of pairs of points, which can be calculated directly. The tests performed on simulated and real datasets confirm the validity and great potential of the method.