scholarly journals Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1004
Author(s):  
Fumitaka Ogushi ◽  
Masashi Matsuoka ◽  
Marco Defilippi ◽  
Paolo Pasquali
Keyword(s):  
Data Processing ◽  
Surface Displacement ◽  
Real Data ◽  
Velocity Spectrum ◽  
Persistent Scatterer Interferometry ◽  
Implementation Phase ◽  
The Real ◽  
Persistent Scatterer ◽  
Non Linear ◽  
Non Parametric

To derive surface displacement, interferometric stacking with synthetic aperture radar (SAR) data is commonly used, and this technique is now in the implementation phase in the real world. Persistent scatterer interferometry (PSI) is one of the most universal approaches among in- terferometric stacking techniques, and non-linear non-parametric PSI (NN-PSI) was proposed to overcome the drawbacks of PSI approaches. The estimation of the non-linear displacements was successfully conducted using NN-PSI. However, the estimation of NN-PSI is not always stable with certain displacements because wider range of the velocity spectrum is used in NN-PSI than the conventional approaches; therefore, a calculation procedure and parameter optimization are needed to consider. In this paper, optimized parameters and procedures of NN-PSI are proposed, and real data processing with Sentinel-1 in the Kanto region in Japan was conducted. We confirmed that the displacement estimation was comparable to the measurement of the permanent global positioning system (GPS) stations, and the root mean square error between the GPS measurement and NN-PSI estimation was less than 3 mm in two years. The displacement over 2π ambiguity, which the conventional PSI approach wrongly reconstructed, was also quantitatively validated and successfully estimated by NN-PSI. As a result of the real data processing, periodical displacements were also reconstructed through NN-PSI. We concluded that the NN-PSI approach with the proposed parameters and method enabled the estimation of several types of surface displacements that conventional PSI approaches could not reconstruct.

scholarly journals Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach

2019 ◽  
Vol 11 (21) ◽  
pp. 2467
Author(s):  
Ogushi ◽  
Matsuoka ◽  
Defilippi ◽  
Pasquali
Keyword(s):  
Data Processing ◽  
Observation Data ◽  
Maximum Magnitude ◽  
Parametric Approach ◽  
Persistent Scatterer Interferometry ◽  
Surface Displacements ◽  
Persistent Scatterer ◽  
Non Linear ◽  
Simulation Results ◽  
Non Parametric

Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor’s wavelength (2.5λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is –0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.

scholarly journals Monitoring the Vulnerability of the Dam and Dikes in Germano Iron Mining Area after the Collapse of the Tailings Dam of Fundão (Mariana-MG, Brazil) Using DInSAR Techniques with TerraSAR-X Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1507 ◽  
Author(s):  
José Mura ◽  
Fábio Gama ◽  
Waldir Paradella ◽  
Priscila Negrão ◽  
Samuel Carneiro ◽  
...  
Keyword(s):  
Surface Displacement ◽  
Mining Area ◽  
Mining Waste ◽  
Tailings Dam ◽  
Persistent Scatterer Interferometry ◽  
Spatiotemporal Information ◽  
Heavy Equipment ◽  
Persistent Scatterer ◽  
Iron Mining ◽  
Small Baseline

The Fundão tailings dam in the Germano iron mining complex (Mariana, Brazil) collapsed on the afternoon of 5 November 2015, and around 32.6 million cubic meters of mining waste spilled from the dam, causing polluion with mining waste along a trajectory of 668 km, extending to the Atlantic Ocean. The Sela & Tulipa and Selinha dikes, and the main Germano tailings dam, were directly or indirectly affected by the accident. This work presents an investigation using Advanced-Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques for risk assessment in these critical structures during 18 months after the catastrophic event. The approach was based on the integration of SBAS (Small Baseline Subset) and PSI (Persistent Scatterer Interferometry) techniques, aiming at detecting linear and nonlinear ground displacements in these mining structures. It used a set of 48 TerraSAR-X images acquired on ascending mode from 11 November 2015 to 15 May 2017. The results provided by the A-DInSAR analysis indicated an overall stability in the dikes and in the main wall of Germano tailings dam, which is in agreement with in situ topographic monitoring. In addition, it was possible to detect areas within the reservoir showing accumulated values of up to −125 mm of subsidence, probably caused by settlements of the waste dry material due to the interruption of the mining waste deposition, and values up to −80 mm on auxiliary dikes, probably caused by continuous traffic of heavy equipment. The spatiotemporal information of surface displacement of this large mining structure can be used for future operational planning and risk control.

scholarly journals Applying persistent scatterer interferometry for surface displacement mapping in the Azul open pit manganese mine (Amazon region) with TerraSAR-X StripMap data

2015 ◽  
Vol 9 (1) ◽  
pp. 095978 ◽  
Author(s):  
Carolina de Athayde Pinto ◽  
Waldir Renato Paradella ◽  
José Claudio Mura ◽  
Fabio Furlan Gama ◽  
Athos Ribeiro dos Santos ◽  
...  
Keyword(s):  
Surface Displacement ◽  
Amazon Region ◽  
Open Pit ◽  
Persistent Scatterer Interferometry ◽  
Displacement Mapping ◽  
Persistent Scatterer

scholarly journals Point Information Gain and Multidimensional Data Analysis

2016 ◽  
Author(s):  
Renata Rychtáriková ◽  
Jan Korbel ◽  
Petr Macháček ◽  
Petr Císař ◽  
Jan Urban ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Information Gain ◽  
Real Data ◽  
Entropy Density ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
The Real ◽  
Multidimensional Datasets

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

scholarly journals Determinants of Non-Linear Effects of Fiscal Policy on Output: The Case of Bulgaria

2009 ◽  
Vol 4 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Vladimir Vladimirov ◽  
Maria Neycheva
Keyword(s):  
Fiscal Policy ◽  
Currency Board ◽  
Main Determinant ◽  
The Real ◽  
Real Gdp ◽  
Non Linear ◽  
Short Run ◽  
Linear Effects ◽  
Policy Impacts ◽  
The Government

Determinants of Non-Linear Effects of Fiscal Policy on Output: The Case of BulgariaThe paper illuminates the non-linear effects of the government budget on short-run economic activity. The study shows that in the Bulgarian economy under a Currency Board Arrangement the tax policy impacts the real growth in the standard Keynesian manner. On the other hand, the expenditure policy exhibits non-Keynesian behavior on the short-run output: cuts in government spending accelerate the real GDP growth. The main determinant of this outcome is the size of the discretionary budgetary changes. The results imply that the balanced budget rule improves the sustainability of public finances without assuring a growth-enhancing effect.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

Sign in / Sign up

Export Citation Format

Share Document