ИСТИНА

In recent times InSAR methods of estimation of surface displacements find ever-widening applications. To analyze time series different techniques of identification of persistent scatterers (PSInSAR) can be employed (e.g., Feretti, 2001). However, density of PS in rural areas is usually low (<10-20 PS/sq.km). Along with PS techniques, methods for estimation of displacements of both coherent and decorrelated pixels have been developed. One of the possible approaches to reduce decorrelation is to analyze only interferometric pairs with small temporal or spatial baselines (e.g. SBAS, Berardino et al., 2002). The scatterers identified in such a way were called distributed scatterers (DS). Hooper's method (implemented in the StaMPS /MTI software) falls in between. It is based on assumption of phase stability of scatterers as criterion for identification of persistent scatterers. This makes it possible to increase number of PS in rural areas. Then techniques allowing to identify both PS and DS and employing all possible interferograms were developed (Ferretti et al.,2011; Lanari et al., 2013; Samiei-Esfahany et al. 2016; Wang et al., 2016, etc). This substantially increases spatial density of scatterers, thus, improving unwrapping procedure and accuracy of estimation of displacement fields. Our approach is based on the following. Scatterers on natural terrains are often identified in areas with low (for some periods of time) phase coherence. However, many neighboring pixels show similar reflectivity because they belong to the same natural object. Thus, similar to SqueeSAR (Ferretti et al.,2011) we first reveal spatially connected clusters of statistically homogeneous pixels (SHP). To estimate statistical homogeneity we employed the two sample Kolmogorov–Smirnov test (Feretti, 2011). Phase filtering is performed on the assumption of constant phase within the whole SHP range and expected phase consistency condition using: (A) the Phase Linking method (Monti Guarnieri, Tebaldini, 2008); (B) the method, introduced in (Feretti et al, 2011a) and (C) the Integer Least Squares (ILS) method (Samiei-Esfahany et al. 2016). These algorithms are implemented as a module compatible with StaMPS/MTI (see scheme below). We applied the proposed approach to landslide investigation in the Caucasus (ALOS PALSAR è ENVISAT images) and seismic and volcanic events in the Kuril-Kamchatka subduction zone (KKSZ) (ENVISAT images). Phase filtering (the optimal phase estimation) applied to the stack of Envisat images provided doubled number of scatterers (distributed and persistent) as compared to number of PS obtained by traditional PS Stamps. Application of the ILS method (Ñ) takes more than 5 times as much as using methods (A) and (B).