Synthetic aperture radar interferometry

Abstract

Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. Many physical and geometric parameters of the imaged scene contribute to the grey value of a SAR image pixel. Scene inversion suffers from this high ambiguity and requires SAR data taken at different wavelength, polarization, time, incidence angle, etc. Interferometric SAR (InSAR) exploits the phase differences of at least two complex-valued SAR images acquired from different orbit positions and/or at different times. The information derived from these interferometric data sets can be used to measure several geophysical quantities, such as topography, deformations (volcanoes, earthquakes, ice fields), glacier flows, ocean currents, vegetation properties, etc. This paper reviews the technology and the signal theoretical aspects of InSAR. Emphasis is given to mathematical imaging models and the statistical properties of the involved quantities. Coherence is shown to be a useful concept for system description and for interferogram quality assessment. As a key step in InSAR signal processing two-dimensional phase unwrapping is discussed in detail. Several interferometric configurations are described and illustrated by real-world examples. A compilation of past, current and future InSAR systems concludes the paper.

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

“…It is worth remarking that the use of conventional multi-look interferograms is a distinctive characteristic of the proposed approach with respect to previous solutions, which are based on constraining the analysis to distributed scatterers, identified through a pixel-by-pixel selection procedure performed on the full resolution complex SAR image spatial grid (Ferretti et al 2011, Parizzi andBrcic 2011). This selection permits to rely on the distributed scattering hypothesis (which is met when no dominant scatterers, such as artificial objects, are present in the resolution cell, Bamler and Hartl (1998)) under which the probability density function of the complex-valued SAR image may be regarded as being a zero-mean multivariate circular normal distribution. Under this assumption, an appropriate maximum likelihood estimation step of the filtered phase values associated with each SAR acquisition can be implemented (Ferretti et al 2011).…”

“…Moreover, the weights w k , ∀k = 1, ..., M represent the estimates of the spatial coherences computed from the phase of the original multi-look interferograms, as follows (Bamler and Hartl 1998):…”

A Motion Compensation Strategy for Airborne Repeat-Pass SAR Data

“…The coregistration procedure can be generalized to stacks of images in different fashions. One solution consists in bringing each element of s into the same geometry as of a single SAR acquisition, forming a one-to-many interferometric network [1,7]. Alternatively, one can generate interferograms from all possible, non-trivial coherent combinations of the elements in s. However, if reducing the impact of interferometric decorrelation is the prime concern, one may restrict the coregistration to those image pairs where the coherence is likely high, i.e.…”

“…Spaceborne repeat-pass radar interferometry (InSAR) and differential interferometric SAR (DInSAR) are established techniques applied in the generation of Digital Elevation Models (DEMs) and for measuring deformations of the Earth surface [1]. As a consequence of the stable trajectory of the spacecraft, SAR image focusing does not introduce undesired artefacts due to uncompensated, azimuth time-variant inaccuracies in the assumed sensor position [2].…”

“…As a figure of merit for the case of DInSAR, the interferometric phase noise needs to be below 3 • to reach a millimetre accuracy at L-band [4]. Low interferometric coherence can be induced by large differences in the acquisition geometry [1] and/or by changes in the scatterers arrangements and/or dielectric properties [1]. This letter presents a refined processing strategy tailored to optimally estimate residual motion errors in stacks of acquisitions, where some interferometric combinations are strongly affected by decorrelation.…”