A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms

By P. Berardino, Gianfranco Fornaro, Riccardo Lanari, Eugenio Sansosti. IEEE Transactions on Geoscience and Remote Sensing, 2002

4,874 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atmospheric Science, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/b89dad1cc6b319f9d98887902c4a2d58426b3914

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Citations in the corpus

Post-seismic motion following the 1997 Manyi (Tibet) earthquake: InSAR observations and modelling (2007, methodology): “For each pixel, the line-of-sight displacement relative to a reference time is calculated using a least-squares approach modified from Schmidt & Bürgmann (1998) and Berardino et al. (2002).”
Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives (2014, methodology): “Alternatively, the Small Baseline Subset (SBAS) [83] and similar methods do not limit the analysis to CT points, but they compute”
Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images (2003, methodology): “The basis for the linear estimation of movement is the adjustment of a linear model, which considers the linear velocity of displacement and the DEM error, to the available data in a similar way as done in the preceding methods [ 10 ], [12], [13].”
Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models (2009, methodology): “…et al., 2001; Schmidt et al., 2005) or applying temporal low-pass filtering or smoothing in time series analysis (e.g., Ferretti et al., 2001; Berardino et al., 2002; Schmidt and Bürgmann, 2003) are effective methods to reduce the noise on the observed phase, provided that the ground motion…”
Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives (2014, methodology): “…Interferometry (MTInSAR orMTI) methods (e.g. Permanent Scatterers Interferometry — PSInSAR™, Ferretti et al., 2001, Small Baseline Subset — SBAS, Berardino et al, 2002; Usai, 2003), which overcome some of the limitations of conventional differential SAR interferometry (DInSAR) and iii) the…”
Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning (2017, background): “Geometrical and temporal decorrelation and atmospheric effects caused by the variation of the phase reflectivity value of some radar targets reduce the reliability of the D-InSAR technique (Berardino et al., 2002).”
Gravity and magma induced spreading of Mount Etna volcano revealed by satellite radar interferometry (2004, methodology): “Average linear velocities for each pixel are shown with an average coherence greater than or equal to 0.6, following the analysis technique of Berardino et al. [2002] .”
A review of ground-based SAR interferometry for deformation measurement (2014, methodology): “, see Berardino et al. (2002), and the amplitude-based selection (Ferretti et al.”
Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction (2019, methodology): “…homepage: www.elsevier.com/locate/cageo https://doi.org/10.1016/j.cageo.2019.104331 Received 14 May 2019; Received in revised form 15 September 2019; Accepted 21 September 2019 Computers and Geosciences 133 (2019) 104331 baseline subsets (SBAS) (Berardino et al., 2002; Schmidt and Bürgmann, 2003).”
A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR (2011, background): “…for areas characterized by DS, where, however, simple moving average filters can strongly improve the SNR of the interferometric fringes at the cost of a decrease in resolution—a well known fact by those who typically apply interferogram stacking techniques [21], [22] or SBAS [16], [23].”
Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics (2013, methodology): “An example is SBAS (small baseline subset) InSAR [624,625] which uses only”
LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor (2020, methodology): “, high-pass in time and low-pass in space) is used to separate spatially correlated noise from deformation signals under the assumption that the noise component is uncorrelated in time [3,5,7].”
On the Exploitation of Target Statistics for SAR Interferometry Applications (2008, neutral): “cept of small baseline subsets [7], [8].”
On the Extension of the Minimum Cost Flow Algorithm for Phase Unwrapping of Multitemporal Differential SAR Interferograms (2006, methodology): “To this end, we have repeated the overall processing operation based on the extended MCF approach but assuming no 2π integer correction, i.e., with Kopt = 0.”
A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms (2004, methodology): “Let us now assume, similarly to [5], that the DIFSAR stack is composed of interferograms, with the following two index vectors:”