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A Combined Multi-Temporal InSAR Method Incorporating Persistent Scatterer and Small Baseline Approaches

Andrew Hooper(1)

(1) University of Iceland, Askja Building, 101 Reykjavik, Iceland


InSAR techniques that process data from multiple acquisitions in time enable both the extraction of deformation time series and a reduction of error terms present in single interferograms. There are currently two broad categories of methods that deal with multi-temporal images: persistent scatterer (PS) methods and small baseline (SB) methods, each of which is optimised for a different ground resolution element scattering model. In a SAR image, the value for each pixel is the coherent sum of contributions from all scatterers within the associated ground resolution element. Relative movement of these scatterers or change in look or squint angle causes the scatterer contributions to sum differently, an effect known as decorrelation. For ground resolution elements containing a persistent dominant scatterer the phase due to decorrelation varies little with time even if the dimmer scatterers move with respect to the dominant scatterer. Furthermore, the variation is also small when viewed from different look and squint angles. This is the principle behind a PS pixel. For resolution elements containing a distribution of scatterers, on the other hand, phase variation due to decorrelation is usually large enough to obscure the underlying signal. However, by forming interferograms only between images separated by a short time interval the decorrelation in time is minimized, and for some resolution elements may be small enough that the underlying signal is still detectable. Furthermore, if the difference in look and squint angle between each pair of images is not too large, the corresponding geometric decorrelation can be reduced by band-pass filtering in range and azimuth. Pixels whose phase when filtered decorrelates little over short time intervals are the targets of SB methods. Note that for pixels dominated by a single scatterer, the effect of range and azimuth filtering is to increase decorrelation. Nevertheless, the decorrelation may still be low enough that a PS pixel is also selected by SB methods.

There has been some debate about the relative merits of PS and SB approaches. However, as they are optimized for different scattering models, the two approaches are in fact complementary, at least in the usual case where a data set contains pixels with a range of scattering characteristics. Here, a new algorithm is presented that combines both PS and SB approaches to maximize the spatial coverage of useable signal. Improvement of the spatial coverage is important not only because it increases the resolution of any deformation signal, but also because it allows for more reliable estimation of integer phase-cycle ambiguities present in the data (phase-unwrapping). For PS selection the method of Hooper et al. [JGR, 2007] is used and for the SB processing a new algorithm is applied that operates on single-look pixels. After pixel selection, the two data sets are combined and further processing is performed to isolate the deformation signal. Combination of the two data sets increases the both number of pixels with useable signal and also increases the SNR for pixels selected by both methods. The combined algorithm therefore extracts the deformation signal over a greater area and more reliably than either method alone. The new algorithm is applied to ERS data acquired over Eyjafjallajökull volcano in Iceland, which experienced intrusive episodes during 1994 and 1999–2000. Even though deformation rates vary considerably with time, a time series for the displacement field is successfully retrieved.


Workshop presentation

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry