A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena

This paper presents a new technique for mapping mean deformation velocity in highly decorrelated areas with known deformation patterns, exploiting high-resolution synthetic aperture radar (SAR) data. The implemented method is based on distributed scatterers and first makes use of the Anderson-Darling (AD) statistical test to identify homogenous patches of pixels based on SAR amplitude images. Then, a robust object adaptive parameter estimation is performed to estimate the local gradients of deformation velocity and the local gradients of residual DEM in range and azimuth directions for these patches, utilizing small baseline differential interferograms. Finally, the information obtained from different patches is connected to get the deformation velocity, via a 2-D model-based deformation integration using Bayesian inference. Compared with published multitemporal interferometric work, the main advantage of the newly developed algorithm is that it does not require any phase unwrapping, and because of this, the method is largely insensitive to decorrelation phenomenon occurring in natural terrains and the availability of persistent scatterers (PSs), in contrast to the coherent stacking techniques such as PS interferometry, small baseline subset algorithm, and SqueeSAR. The method is computationally inexpensive with respect to SqueeSAR as only the small baseline interferograms are used for the processing. The method provides spatially dense deformation velocity maps at a suitable object resolution, as compared with a few measured points provided by the stacking techniques in difficult decorrelated regions. High Resolution Spotlight TerraSAR-X data set of Lueneburg in Germany is used as a processing example of this technique.