scholarly journals Long-Term Land Deformation Monitoring Using Quasi-Persistent Scatterer (Q-PS) Technique Observed by Sentinel-1A: Case Study Kelok Sembilan

2019 ◽  
Author(s):  
Pakhrur Razi
Keyword(s):  
High Performance ◽  
Ground Truth ◽  
Deformation Monitoring ◽  
Acquisition Time ◽  
Satellite Orbits ◽  
Mountainous Area ◽  
Ground Truth Data ◽  
Persistent Scatterer ◽  
Land Deformation ◽  
The Impact

Located on the mountainous area, Kelok Sembilan flyover area in West Sumatra, Indonesia has a long history of land deformation, therefore monitoring and analyzing as continuously is a necessity to minimize the impact. Notably, in the rainy season, the land deformation occurs along this area. The zone is crucial as the center of transportation connection in the middle of Sumatra. Quasi-Persistent Scatterer (Q-PS) Interferometry technique was applied for extracting information of land deformation on the field from time to time. Not only does the method have high performance for detecting land deformation but also improve the number of PS point, especially in a non-urban area. This research supported by 90 scenes of Sentinel-1A (C-band) taken from October 2014 to November 2017 for ascending and descending orbit with VV and VH polarization in 5 × 20 m (range × azimuth) resolution. Both satellite orbits detected two critical locations of land deformation namely as zone A and Zone B, which located in positive steep slope where there is more than 500 mm movement in the Line of Sight (LOS) during acquisition time. Deformations in the vertical and horizontal direction for both zone, are 778.9 mm, 795.7 mm and 730.5 mm, 751.7 mm, respectively. Finally, the results were confirmed by ground truth data using Unmanned Aerial Vehicle (UAV) observation.

scholarly journals A flexible ChIP-sequencing simulation toolkit

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
An Zheng ◽  
Michael Lamkin ◽  
Yutong Qiu ◽  
Kevin Ren ◽  
Alon Goren ◽  
...  
Keyword(s):  
Ground Truth ◽  
Peak Calling ◽  
Simulation Framework ◽  
Experimental Conditions ◽  
Ground Truth Data ◽  
Chip Sequencing ◽  
Genome Wide ◽  
Experimental Parameters ◽  
Differential Binding ◽  
The Impact

Abstract Background A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. Results We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips. Conclusions ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

scholarly journals Efficient ancestry and mutation simulation with msprime 1.0

Genetics ◽  
2021 ◽  
Author(s):  
Franz Baumdicker ◽  
Gertjan Bisschop ◽  
Daniel Goldstein ◽  
Graham Gower ◽  
Aaron P Ragsdale ◽  
...  
Keyword(s):  
Population Genetics ◽  
Data Structure ◽  
Software Quality ◽  
High Performance ◽  
Sequence Data ◽  
Ground Truth ◽  
Development Model ◽  
Ground Truth Data ◽  
Open Source Development ◽  
Memory Efficient

Abstract Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

scholarly journals An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 819-826 ◽  
Author(s):  
N. Milisavljevic ◽  
D. Closson ◽  
F. Holecz ◽  
F. Collivignarelli ◽  
P. Pasquali
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Before And After ◽  
The Impact ◽  
Aperture Radar

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. <br><br> The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. <br><br> A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

scholarly journals Improving PSI Processing of Mining Induced Large Deformations with External Models

2020 ◽  
Vol 12 (19) ◽  
pp. 3145
Author(s):  
Sen Du ◽  
Jordi J. Mallorqui ◽  
Hongdong Fan ◽  
Meinan Zheng
Keyword(s):  
Ground Deformation ◽  
Ground Truth ◽  
Deformation Monitoring ◽  
Persistent Scatterer Interferometry ◽  
Mining Operations ◽  
Sar Images ◽  
Temporal Behaviour ◽  
Persistent Scatterer ◽  
Large Gradient ◽  
External Model

Ground subsidences, either caused by natural phenomena or human activities, can threaten the safety of nearby infrastructures and residents. Among the different causes, mining operations can trigger strong subsidence phenomena with a fast nonlinear temporal behaviour. Therefore, a reliable and precise deformation monitoring is of great significance for safe mining and protection of facilities located above or near the mined-out area. Persistent Scatterer Interferometry (PSI) is a technique that uses stacks Synthetic Aperture Radar (SAR) images to remotely monitor the ground deformation of large areas with a high degree of precision at a reasonable cost. Unfortunately, PSI presents limitations when monitoring large gradient deformations when there is phase ambiguity among adjacent Persistent Scatterer (PS) points. In this paper, an improvement of PSI processing, named as External Model-based Deformation Decomposition PSI (EMDD-PSI), is proposed to address this limitation by taking advantage of an external model. The proposed method first uses interferograms generated from SAR Single Look Complex (SLC) images to optimize the parameter adjustments of the external model. Then, the modelled spatial distribution of subsidence is utilized to reduce the fringes of the interferograms generated from the SAR images and to ease the PSI processing. Finally, the ground deformation is retrieved by jointly adding the external model and PSI results. In this paper, fourteen Radarsat-2 SAR images over Fengfeng mining area (China) are used to demonstrate the capabilities of the proposed method. The results are evaluated by comparing them with leveling data of the area covering the same temporal period. Results have shown that, after the optimization, the model is able to mimic the real deformation and the fringes of the interferograms can be effectively reduced. As a consequence, the large gradient deformation then can be better retrieved with the preservation of the nonlinear subsidence term. The ground truth shows that, comparing with the classical PSI and PSI with unadjusted parameters, the proposed scheme reduces the error by 35.2% and 20.4%, respectively.

scholarly journals A NEW STEREO DENSE MATCHING BENCHMARK DATASET FOR DEEP LEARNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 405-412
Author(s):  
T. Wu ◽  
B. Vallet ◽  
M. Pierrot-Deseilligny ◽  
E. Rupnik
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Fine Tuning ◽  
Training Dataset ◽  
Ground Truth Data ◽  
Aerial Photogrammetry ◽  
Dense Matching ◽  
Benchmark Datasets ◽  
3D Scene ◽  
The Impact

Abstract. Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper, we publish a stereo dense matching dataset based on ISPRS Vaihingen dataset, and use it to evaluate some traditional and deep learning based methods. The evaluation shows that learning-based methods outperform traditional methods significantly when the fine tuning is done on a similar landscape. The benchmark also investigates the impact of the base to height ratio on the performance of the evaluated methods. The dataset can be found in https://github.com/whuwuteng/benchmark_ISPRS2021.

scholarly journals Long-Term Land Deformation Monitoring Using Quasi-Persistent Scatterer (Q-PS) Technique Observed by Sentinel-1A: Case Study Kelok Sembilan

2018 ◽  
Vol 07 (04) ◽  
pp. 277-289 ◽  
Author(s):  
Pakhrur Razi ◽  
Josaphat Tetuko Sri Sumantyo ◽  
Daniele Perissin ◽  
Hiroaki Kuze
Keyword(s):  
Deformation Monitoring ◽  
Persistent Scatterer ◽  
Land Deformation

scholarly journals EXTRACTING AND EVALUATING CLUSTERS IN DINSAR DEFORMATION DATA ON SINGLE BUILDINGS

2020 ◽  
Vol V-3-2020 ◽  
pp. 157-163
Author(s):  
P. J. Schneider ◽  
R. Khamis ◽  
U. Soergel
Keyword(s):  
Ground Truth ◽  
Deformation Space ◽  
Powerful Method ◽  
Persistent Scatterer Interferometry ◽  
Ground Truth Data ◽  
Precise Levelling ◽  
The Past ◽  
Persistent Scatterer ◽  
Displacement Patterns ◽  
Insight Into

Abstract. In the past two decades persistent scatterer interferometry (PSI) has become a well understood and powerful method to monitor the deformations of man-made structures. PSI can derive displacement histories of thousands of scattered points on a single building with accuracy of a few millimetre per year, by analysing space-borne SAR data. In this paper, we present a method to cluster PS points on a single building into segments which show the same deformation behavior. The spatial distribution of those clusters gives an insight into the structural behavior of a building. We use dimensionality reduction to visualize the clusters in the deformation space. The comparison of our extracted displacement patterns with ground truth data from precise levelling and 3D tachymetry confirms the plausibility of our remote sensing method.

scholarly journals LANDSLIDES MONITORING WITH TIME SERIES OF SENTINEL-1 IMAGERY IN YEN BAI PROVINCE-VIETNAM

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 197-203
Author(s):  
V. A. Tran ◽  
X. Q. Truong ◽  
D. A. Nguyen ◽  
L. Longoni ◽  
V. Yordanov
Keyword(s):  
Field Survey ◽  
Mineral Resources ◽  
Deformation Monitoring ◽  
Mitigation Measures ◽  
Mountainous Area ◽  
The Road ◽  
Average Value ◽  
Processing Data ◽  
Land Deformation ◽  
Sar Processing

Abstract. This paper presents an application of PS-InSAR method for determining landslide displacement velocity in Van Yen district, Yen Bai province, Vietnam. The used tools for processing data is a combination of two free software, SNAP 7.0 and STaMPS 4.1. With 27 Sentinel-1A images in descending direction acquired from 11th January 2019 to 1st March 2021, the landslide displacement values were calculated and exported. There were locations in which landslides correctly appeared, such as Lang Thip, Xuan Tam, Chau Que Ha, Phong Du Thuong communes and along provincial road 151. Landslide rate is determined from SAR image series with average value less than 16.5 mm/y in places with high terrain and steep slope. The distribution of permanent scatter (PS) points for landslides often appeared along the road slopes, especially the inter-communal and inter-provincial roads that have not been reinforced with structural mitigation measures. In 2013 a field survey was conducted by the Vietnam Institute of Geosciences and Mineral Resources for this area which was used to validate the results from SAR processing. Landslide velocity charts at certain landslide sites were derived. The current study demonstrated the feasibility of the method as well as the usage of Sentinel-1 data for land deformation monitoring in the mountainous area.

scholarly journals 3D Land Mapping and Land Deformation Monitoring Using Persistent Scatterer Interferometry (PSI) ALOS PALSAR: Validated by Geodetic GPS and UAV

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 12395-12404 ◽  
Author(s):  
Pakhrur Razi ◽  
Josaphat Tetuko Sri Sumantyo ◽  
Daniele Perissin ◽  
Hiroaki Kuze ◽  
Ming Yam Chua ◽  
...  
Keyword(s):  
Deformation Monitoring ◽  
Persistent Scatterer Interferometry ◽  
Alos Palsar ◽  
Persistent Scatterer ◽  
Land Deformation

scholarly journals Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging Registry

2021 ◽  
Author(s):  
Kareem Wahid ◽  
Sara Ahmed ◽  
Renjie He ◽  
Lisanne van Dijk ◽  
Jonas Teuwen ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
False Negative ◽  
Model Performance ◽  
Ground Truth ◽  
Turing Test ◽  
Multiparametric Mri ◽  
Dice Similarity Coefficient ◽  
Channel Effects ◽  
The Impact

Background and Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. Materials and Methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2+T1, T2+ADC, T2+Ktrans, T2+Ve, all 5 channels [ALL]) primarily using the Dice similarity coefficient (DSC). Sensitivity, positive predictive value, Hausdorff distance (HD), false-negative DSC (FND), false-positive DSC, surface DSC, 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a Turing test using physician observers. Results: Models yielded mean DSCs from 0.71 (ALL) to 0.73 (T2+T1). Compared to the T2 model, performance was significantly improved for HD, FND, sensitivity, surface DSC, and 95% HD for the T2+T1 model (p<0.05) and for FND for the T2+Ve and ALL models (p<0.05). There were no differences between ground truth and DL-generated segmentations for all observers (p>0.05). Conclusion: DL using mpMRI provides high-quality segmentations of OPC GTVp. Incorporating additional mpMRI channels may increase the performance of certain evaluation metrics. This pilot study is a promising step towards fully automated MR-guided OPC radiotherapy.

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