In this paper, an advanced technique for the generation of deformation maps using SAR (Synthetic Aperture Radar) data is presented. The algorithm estimates the linear and non-linear components of the displacement, the error of the Digital Elevation Model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting non-uniform mesh tessellated with the Delaunay triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the non-linear component of movement and atmospheric artifacts with alternate filtering techniques in both temporal and spatial domains. The method presents high flexibility with respect the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with ERS SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.
In this paper we evaluate a combined phase unwrapping method that improves the results in difficult areas (containing noise and discontinuities) taking advantage of the Region Growing (RG) and Weighted Least Mean Square (WLMS) algorithms. The performance of the combined method is based on the following steps. Starting from the wrapped phase, a binary mask is calculated using a first RG solution, in order to generate a mask which is able to consider low-quality pixels. Afterwards, the WLMS solution is calculated using the wrapped phase and the RG mask. To obtain this WLMS solution it is not necessary to compute many iterations, since the objective of this step is just to ease the subsequent RG process. Therefore, the calculation time is smaller than with a WLMS solution separately, because it needs a larger number of iterations to converge. As commented, at this stage the RG algorithm is applied in order to unwrap the error of the WLMS solution, which is expected to have less noise than the input interferogram. Therefore, the RG is able to handle its values in a relatively easy manner. Finally, the first WLMS solution and the unwrapped error are added to obtain the desired unwrapped phase.