Deformation monitoring over large areas with multipass differential SAR interferometry: a new approach based on the use of spatial differences

2009 ◽  
Vol 30 (6) ◽  
pp. 1455-1478 ◽  
Author(s):  
G. Fornaro ◽  
A. Pauciullo ◽  
F. Serafino
Keyword(s):  
Deformation Monitoring ◽  
Sar Interferometry ◽  
New Approach ◽  
Spatial Differences ◽  
Differential Sar Interferometry

scholarly journals Generalized-Capon Method for Diff-Tomo SAR Analyses of Decorrelating Scatterers

2019 ◽  
Vol 11 (4) ◽  
pp. 412 ◽  
Author(s):  
Fabrizio Lombardini ◽  
Francesco Cai
Keyword(s):  
Remote Sensing ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Full Model ◽  
Temporal Decorrelation ◽  
Sar Imaging ◽  
Profile Height ◽  
Differential Sar Interferometry ◽  
Dynamical Information

In synthetic aperture radar (SAR) remote sensing, Differential Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography. Diff-Tomo produces advanced 4D (3D+Time) SAR imaging capabilities, extensively applied to urban deformation monitoring. More recently, it has been shown that, through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating scatterers, the important decorrelation-robust forest Tomography functionality is possible. To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an adaptive, just semi-parametric, generalized-Capon Diff-Tomo method, first conceived at University of Pisa in 2013, for joint extraction of height and dynamical information of natural distributed (volumetric) scatterers, with its formalization and a series of insights. Particular reference is given to the important functionality of the separation of different decorrelation mechanisms in forest layers. Representative simulated and P-band forest data sample results are also shown. The new Diff-Tomo method is getting a flexible and rich decorrelation-robust Tomography functionality, and is able to profile height-varying temporal decorrelation, for significantly distributed scatterers.

scholarly journals Spaceborne Differential SAR Interferometry: Data Analysis Tools for Deformation Measurement

2011 ◽  
Vol 3 (2) ◽  
pp. 305-318 ◽  
Author(s):  
Michele Crosetto ◽  
Oriol Monserrat ◽  
María Cuevas ◽  
Bruno Crippa
Keyword(s):  
Data Analysis ◽  
Deformation Measurement ◽  
Sar Interferometry ◽  
Analysis Tools ◽  
Differential Sar Interferometry

An Improved Wavelet Threshold De-Noising Data Processing Method Research in Deformation Monitoring

2011 ◽  
Vol 90-93 ◽  
pp. 2858-2863
Author(s):  
Wei Li ◽  
Xu Wang
Keyword(s):  
Data Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
New Approach ◽  
Data Processing Method ◽  
Soft Threshold ◽  
Fixed Bias ◽  
Better Than

Due to the soft and hard threshold function exist shortcomings. This will reduce the performance in wavelet de-noising. in order to solve this problem，This article proposes Modulus square approach. the new approach avoids the discontinuity of the hard threshold function and also decreases the fixed bias between the estimated wavelet coefficients and the wavelet coefficients of the soft-threshold method．Simulation results show that SNR and MSE are better than simply using soft and hard threshold，having good de-noising effect in Deformation Monitoring.

scholarly journals Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas

2018 ◽  
Vol 10 (9) ◽  
pp. 1360 ◽  
Author(s):  
Tazio Strozzi ◽  
Sofia Antonova ◽  
Frank Günther ◽  
Eva Mätzler ◽  
Gonçalo Vieira ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Displacement ◽  
Deformation Monitoring ◽  
In Situ Measurements ◽  
River Delta ◽  
Sar Interferometry ◽  
The Arctic ◽  
Lena River ◽  
Lena River Delta

Low-land permafrost areas are subject to intense freeze-thaw cycles and characterized by remarkable surface displacement. We used Sentinel-1 SAR interferometry (InSAR) in order to analyse the summer surface displacement over four spots in the Arctic and Antarctica since 2015. Choosing floodplain or outcrop areas as the reference for the InSAR relative deformation measurements, we found maximum subsidence of about 3 to 10 cm during the thawing season with generally high spatial variability. Sentinel-1 time-series of interferograms with 6–12 day time intervals highlight that subsidence is often occurring rather quickly within roughly one month in early summer. Intercomparison of summer subsidence from Sentinel-1 in 2017 with TerraSAR-X in 2013 over part of the Lena River Delta (Russia) shows a high spatial agreement between both SAR systems. A comparison with in-situ measurements for the summer of 2014 over the Lena River Delta indicates a pronounced downward movement of several centimetres in both cases but does not reveal a spatial correspondence between InSAR and local in-situ measurements. For the reconstruction of longer time-series of deformation, yearly Sentinel-1 interferograms from the end of the summer were considered. However, in order to infer an effective subsidence of the surface through melting of excess ice layers over multi-annual scales with Sentinel-1, a longer observation time period is necessary.

scholarly journals A comparative case study of MTInSAR approaches for deformation monitoring of the cultural landscape of the Shanhaiguan section of the Great Wall

2021 ◽  
Author(s):  
Hang Xu ◽  
Fulong Chen ◽  
Wei Zhou
Keyword(s):  
High Resolution ◽  
Linear Structure ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Comparative Case Study ◽  
Rock Falls ◽  
High Resolution Data ◽  
Heritage Sites ◽  
Great Wall

Abstract The Great Wall of China is one of the largest architectural heritage sites globally, and its sustainability is a significant concern. However, its large extent and diverse characteristics cause challenges for deformation monitoring. In this study, the Shanhaiguan section of the Great Wall was investigated in a case study to ascertain the damage and potential hazards of the architectural site. Two standard multi-temporal synthetic aperture radar interferometry (MTInSAR) technologies, including persistent scatterer SAR interferometry (PSInSAR) and small baseline subset (SBAS) SAR interferometry, were used for deformation monitoring using high-resolution TerraSAR-X data acquired in 2015–2017. The results of the two MTInSAR approaches revealed the health condition of the Great Wall. The Shanhaiguan section was stable, but local instabilities caused by rock falls were detected in some mountainous areas. In addition, the applicability of PSInSAR and SBAS was evaluated. The performance analysis of the two approaches indicated that a more reliable and adaptable MTInSAR technique needs to be developed for monitoring the Great Wall. This study demonstrates the potential of MTInSAR technology with high-resolution data for the health diagnosis of heritage sites with a linear structure, such as the Great Wall.

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