# A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements
## 2 DInSAR Techniques
Such advanced DInSAR approaches are based on the exploitation of long temporal sequences of SAR images collected over an investigated area, with the aim of following the temporal evolution of the detected displacements through the generation of deformation time-series, and to mitigate the effects of possible atmospheric artifacts [26; 27; 28; 29; 30; 31; 32; 33; 34]. In particular, the so-called advanced DInSAR techniques are based on a proper combination of a set of multi-temporal differential interferograms relevant to an area of interest, to compute the deformation time-series of the observed zone and the corresponding mean deformation velocity map. In recent decades, several advanced (also referred to as multi-temporal) DInSAR algorithms, characterized by different interferograms selection/generation criteria, have been developed; they are usually grouped into two main categories, commonly referred to as Persistent Scatterer (PS) [26; 27; 28] and Small Baseline (SB) techniques [29; 30; 31], even though a solution that incorporates both PS and SB approaches has also been proposed [32; 33]. The PS-based approaches are focused on the selection of all the interferometric data pairs with respect to one common master image, without imposing any baseline constraint on the interferograms used; accordingly, only single dominant scatterers are analysed, which are less affected by the temporal and spatial noise effects (referred to  
Figure 1: Pictorial representation of the DInSAR scenario; note that in this case we are assuming the ideal case of subsequent acquisitions carried out from the same orbital path. as decorrelation) which characterize the DInSAR interferograms [23]. Conversely, the SB algorithms investigate distributed scatterers (DS), by properly selecting multi-temporal interferometric data pairs characterized by small spatial and temporal baselines; this selection requires the introduction of constraints on the maximum allowed spatial and temporal baseline values of the DInSAR interferograms but permits mitigation of the decorrelation phenomena that affect the interferometric pairs, and, therefore, to significantly increase the spatial density of the retrieved DInSAR measurements, especially in semi-urbanized and rural areas. Among these SB methods, the SBAS approach [29] has successfully been applied to detect surface deformations caused by several natural and human-induced hazards [50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67]. It relies on the proper selection of a large number of SAR data pairs, which are used to generate a multi-temporal sequence of DInSAR interferograms with small temporal and spatial baselines in order to mitigate the decorrelation effects (see Figure 27). The appropriate combination of the computed interferograms allows retrieving information on the mean deformation velocity and the corresponding time-series for each pixel preserving the phase information (referred to as coherent pixel), with an accuracy of about 1-2 mm/year for the mean deformation velocity, and 5-10 mm for the single deformation measurements [68; 69]. In particular, the SBAS-DInSAR technique permits easy retrieval of the deformation time-series relevant to each one of the investigated coherent pixels through the inversion of a linear system of equations by applying the Singular Value Decomposition (SVD) method [70] searching for a least squares solution with a minimum norm energy constraint [29].