Contextual Information-Based Multichannel Synthetic Aperture Radar Interferometry: Addressing DEM reconstruction using contextual information

Abstract

Interferometric synthetic aperture radar (InSAR) systems are capable of providing an estimate of the digital elevation model (DEM) of the imaged ground scene. This is usually done by means of a phase unwrapping (PU) operation. In the absence of additional regularity constraints, PU is an ill-posed problem, because the solution is not unique. Multichannel (MCh) techniques, using stacks of images of the same scene, can be used for restoring the solution uniqueness and reducing the effect of phase noise. Moreover, statistical techniques exploiting the contextual information contained in the data can provide satisfactory results. In this article, an overview of the main MCh statistical DEM reconstruction methods, developed both in the classical and in the Bayesian estimation framework, is presented. In particular, the effectiveness of the exploitation of contextual statistical models is shown by means of numerical experiments on simulated and real data sets.