Noise immune least squares phase unwrapping based on two dimensional frequency estimation

Least squares methods provide a smart global solution to the two dimensional phase unwrapping problem, but results obtained on synthetic aperture radar (SAR) interferograms are usually undervalued because of several kinds of perturbations. In this paper, we analyze the bias due to noise corruption of wrapped phase differences. To avoid it and to prevent the solution from the influence of inconsistent areas, we propose to use an estimation of the local frequency as phase gradient and the associated measure of confidence as a mask. With these robust input data, weighted least squares unwrapping becomes an automatic method useful for SAR interferometric applications