scholarly journals Automated classification of Persistent Scatterers Interferometry time series

2013 ◽  
Vol 13 (8) ◽  
pp. 1945-1958 ◽  
Author(s):  
M. Berti ◽  
A. Corsini ◽  
S. Franceschini ◽  
J. P. Iannacone
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Ground Deformation ◽  
Statistical Tests ◽  
Automated Classification ◽  
Linear Quadratic ◽  
Mean Velocity ◽  
Slope Movements ◽  
Persistent Scatterers

Abstract. We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, or uplifts due to deep injections in reservoirs.

scholarly journals Automated classification of Persistent Scatterers Interferometry time-series

2013 ◽  
Vol 1 (1) ◽  
pp. 207-246 ◽  
Author(s):  
M. Berti ◽  
A. Corsini ◽  
S. Franceschini ◽  
J. P. Iannacone
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Ground Deformation ◽  
Statistical Tests ◽  
Automated Classification ◽  
Linear Quadratic ◽  
Mean Velocity ◽  
Slope Movements ◽  
Persistent Scatterers

Abstract. We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends (such as uncorrelated, linear, quadratic, bilinear and discontinuous) that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, uplifts due to deep injections in reservoirs.

Classification of landslide activity based on spaceborne interferometric SAR at the Moselle Valley, Germany

2021 ◽  
Author(s):  
Andre C. Kalia
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Statistical Tests ◽  
Spatial Density ◽  
Deformation Pattern ◽  
Visual Interpretation ◽  
Landslide Activity ◽  
Spatial Coverage ◽  
Interferometric Sar

<p>Landslide activity is an important information for landslide hazard assessment. However, an information gap regarding up to date landslide activity is often present. Advanced differential interferometric SAR processing techniques (A-DInSAR), e.g. Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) are able to measure surface displacements with high precision, large spatial coverage and high spatial sampling density. Although the huge amount of measurement points is clearly an improvement, the practical usage is mainly based on visual interpretation. This is time-consuming, subjective and error prone due to e.g. outliers. The motivation of this work is to increase the automatization with respect to the information extraction regarding landslide activity.</p><p>This study focuses on the spatial density of multiple PSI/SBAS results and a post-processing workflow to semi-automatically detect active landslides. The proposed detection of active landslides is based on the detection of Active Deformation Areas (ADA) and a subsequent classification of the time series. The detection of ADA consists of a filtering of the A-DInSAR data, a velocity threshold and a spatial clustering algorithm (Barra et al., 2017). The classification of the A-DInSAR time series uses a conditional sequence of statistical tests to classify the time series into a-priori defined deformation patterns (Berti et al., 2013). Field investigations and thematic data verify the plausibility of the results. Subsequently the classification results are combined to provide a layer consisting of ADA including information regarding the deformation pattern through time.</p>

scholarly journals Automated classification of schools of the silver cyprinid Rastrineobola argentea in Lake Victoria acoustic survey data using random forests

2020 ◽  
Vol 77 (4) ◽  
pp. 1379-1390 ◽  
Author(s):  
Roland Proud ◽  
Richard Mangeni-Sande ◽  
Robert J Kayanda ◽  
Martin J Cox ◽  
Chrisphine Nyamweya ◽  
...  
Keyword(s):  
Time Series ◽  
Water Column ◽  
Random Forests ◽  
Lake Victoria ◽  
Test Accuracy ◽  
Acoustic Survey ◽  
Automated Classification ◽  
School Based ◽  
Schooling Fish

Abstract Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple “rule” that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have, however, been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 million tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) for 49 081 manually classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.

scholarly journals Surface Deformation Monitoring in Zhengzhou City from 2014 to 2016 Using Time-Series InSAR

2018 ◽  
Vol 10 (11) ◽  
pp. 1731 ◽  
Author(s):  
Zhengjia Zhang ◽  
Chao Wang ◽  
Mengmeng Wang ◽  
Ziwei Wang ◽  
Hong Zhang
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Urban Expansion ◽  
Correction Method ◽  
Deformation Monitoring ◽  
Ground Subsidence ◽  
Significant Information ◽  
Persistent Scatterers ◽  
Deformation Parameters

In recent years, with the development of urban expansion in Zhengzhou city, the underground resources, such as underground water and coal mining, have been exploited greatly, which have resulted in ground subsidence and several environmental issues. In order to study the spatial distribution and temporal changes of ground subsidence of Zhengzhou city, the Interferometric Synthetic Aperture Radar (InSAR) time series analysis technique combining persistent scatterers (PSs) and distributed scatterers (DSs) was proposed and applied. In particular, the orbit and topographic related atmospheric phase errors have been corrected by a phase ramp correction method. Furthermore, the deformation parameters of PSs and DSs are retrieved based on a layered strategy. The deformation and DEM error of PSs are first estimated using conventional PSI method. Then the deformation parameters of DSs are retrieved using an adaptive searching window based on the initial results of PSs. Experimental results show that ground deformation of the study area could be retrieved by the proposed method and the ground deformation is widespread and unevenly distributed with large differences. The deformation rate ranges from −55 to 10 mm/year, and the standard deviation of the results is about 8 mm/year. The observed InSAR results reveal that most of the subsidence areas are in the north and northeast of Zhengzhou city. Furthermore, it is found that the possible factors resulting in the ground subsidence include sediment consolidation, water exploitation, and urban expansion. The result could provide significant information to serve the land subsidence mitigation in Zhengzhou city.

scholarly journals CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 147-154
Author(s):  
M. Evers ◽  
A. Thiele ◽  
H. Hammer ◽  
E. Cadario ◽  
K. Schulz ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Deformation Pattern ◽  
Mean Velocity ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
The Mean

Abstract. Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

scholarly journals Automated Classification of Variable Stars for ASAS Data

2002 ◽  
Vol 185 ◽  
pp. 160-161
Author(s):  
Laurent Eyer ◽  
Cullen Blake
Keyword(s):  
Time Series ◽  
Light Curve ◽  
Large Fraction ◽  
Variable Stars ◽  
Red Giants ◽  
Automated Classification ◽  
Fourier Decomposition ◽  
Large Numbers ◽  
Remarkable Relation

AbstractWith the advent of surveys generating multi-epoch photometry and their discoveries of large numbers of variable stars, the classification of the obtained time series has to be automated. We have developed a classification algorithm for the periodic variable stars using a Bayesian classifier on a Fourier decomposition of the light curve. This algorithm is applied to ASAS (AII Sky Automated Survey, Pojmanski, 2000). In ASAS 85% of the variables are red giants. A remarkable relation between their period and amplitude is found for a large fraction of those stars.

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