scholarly journals Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2298 ◽  
Author(s):  
Fengming Hu ◽  
Freek J. van Leijen ◽  
Ling Chang ◽  
Jicang Wu ◽  
Ramon F. Hanssen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Physical Object ◽  
Synthetic Aperture ◽  
Lidar Data ◽  
Interferometric Synthetic Aperture Radar ◽  
Railway Systems ◽  
Multi Temporal ◽  
Railway System ◽  
Confidence Ellipsoid

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.

A deep learning approach for efficient multi-temporal interferometric synthetic aperture radar (MT-InSAR) processing

2021 ◽  
Author(s):  
Ashutosh Tiwari ◽  
Avadh BIhari Narayan ◽  
Onkar Dikshit
Keyword(s):  
Deep Learning ◽  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Displacement Estimation ◽  
Interferometric Phase ◽  
Selection Step ◽  
Multi Temporal ◽  
Aperture Radar

<p>Multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique has been effectively used to monitor deformation events over the last two decades. The processing steps generally involve pixel selection, phase unwrapping and displacement estimation. The pixel selection step takes most of the processing time, while a reliable method for phase unwrapping is still not available. This study demonstrates the effect of using deep learning (DL) architectures for MT-InSAR processing. The architectures are applied to reduce time computations and further to improve the quality of pixel selection. Some promising results for pixel selection have been shown earlier with the proposed architecture. In this study, we investigate the performance of the proposed architectures on newer datasets with larger temporal interval. To achieve this objective, the models are retrained with interferometric stacks covering larger temporal period and large time steps (for better estimation of interferometric phase components). Pixel selection results are compared with those obtained using open access algorithms used for MT-InSAR processing.</p>

scholarly journals Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes

2020 ◽  
Vol 10 (21) ◽  
pp. 7409 ◽  
Author(s):  
Waldemar Kociuba
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Mineral Resources ◽  
Noise Removal ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Differential Analysis ◽  
Terrestrial Lidar ◽  
Surface Changes

High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m2, which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors.

scholarly journals MASSIVE LINKING OF PS-INSAR DEFORMATIONS TO A NATIONAL AIRBORNE LASER POINT CLOUD

2018 ◽  
Vol XLII-2 ◽  
pp. 1137-1144 ◽  
Author(s):  
A. L. van Natijne ◽  
R. C. Lindenbergh ◽  
R. F. Hanssen
Keyword(s):  
The Netherlands ◽  
Point Cloud ◽  
Deformation Behaviour ◽  
Soft Soil ◽  
Airborne Lidar ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Persistent Scatterers ◽  
Airborne Laser ◽  
The City

Build on soft soil, close to sea level the Netherlands is at high risk for the effects of subsidence and deformation. Interferometric Synthetic Aperture Radar (InSAR) is successfully used to monitor the deformation trends at millimetre level. Unfortunately the InSAR deformation trends suffer from poor geolocation estimates, limiting the ability to link deformation behaviour to objects, such as buildings, streets or bridges. A nationwide, high resolution, airborne LiDAR point cloud is available in the Netherlands. Although the position accuracy of this LiDAR point cloud is to low for deformation estimates, linking the InSAR location to the geometries outlined by the LiDAR point can improve the geolocation estimates of the InSAR trends. To our knowledge no such integration is available as of yet. In this article we outline methods to link deformation estimates to the LiDAR point cloud and give an outlook of possible improvements. As a test we link 3.1 million TerraSAR-X InSAR Persistent Scatterers to 3 billion LiDAR points, covering the city of Delft and surroundings.

scholarly journals UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

2020 ◽  
Vol 12 (22) ◽  
pp. 3829
Author(s):  
Martin Slavík ◽  
Karel Kuželka ◽  
Roman Modlinger ◽  
Ivana Tomášková ◽  
Peter Surový
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Morphological Changes ◽  
Tree Canopy ◽  
High Temporal Resolution ◽  
Human Observer ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Temporal ◽  
Cloud Metrics

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

scholarly journals Multi-Temporal Small Baseline Interferometric SAR Algorithms: Error Budget and Theoretical Performance

2021 ◽  
Vol 13 (4) ◽  
pp. 557
Author(s):  
Antonio Pepe
Keyword(s):  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Error Budget ◽  
Multi Temporal ◽  
Small Baseline ◽  
Time Inconsistent ◽  
Interferometric Sar ◽  
Aperture Radar

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques are well recognized as useful tools for detecting and monitoring Earth’s surface temporal changes. In this work, the fundamentals of error noise propagation and perturbation theories are applied to derive the ground displacement products’ theoretical error bounds of the small baseline (SB) differential interferometric synthetic aperture radar algorithms. A general formulation of the least-squares (LS) optimization problem, representing the SB methods implementation’s core, was adopted in this research study. A particular emphasis was placed on the effects of time-uncorrelated phase unwrapping mistakes and time-inconsistent phase disturbances in sets of SB interferograms, leading to artefacts in the attainable InSAR products. Moreover, this study created the theoretical basis for further developments aimed at quantifying the error budget of the time-uncorrelated phase unwrapping mistakes and studying time-inconsistent phase artefacts for the generation of InSAR data products. Some experiments, performed by considering a sequence of synthetic aperture radar (SAR) images collected by the ASAR sensor onboard the ENVISAT satellite, supported the developed theoretical framework.

scholarly journals Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

2018 ◽  
Author(s):  
Pankaj Kumar ◽  
Paul Lewis ◽  
Conor P. McElhinney
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Data Retrieval ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Efficient Storage ◽  
Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

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