4D change detection based on persistent scatterer interferometry

2016 ◽  
Author(s):  
Chia-Hsiang Yang ◽  
Benson Kipkemboi Kenduiywo ◽  
Uwe Soergel
Keyword(s):  
Change Detection ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterer

scholarly journals CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – A NEW METHOD OF MONITORING BUILDING CHANGES

2016 ◽  
Vol III-7 ◽  
pp. 243-250
Author(s):  
C. H. Yang ◽  
B. K. Kenduiywo ◽  
U. Soergel
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Ground Truth ◽  
Google Earth ◽  
Aerial Images ◽  
Radar Signal ◽  
Persistent Scatterer Interferometry ◽  
Small Surface ◽  
Persistent Scatterer ◽  
Main Station

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.

scholarly journals CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – A NEW METHOD OF MONITORING BUILDING CHANGES

2016 ◽  
Vol III-7 ◽  
pp. 243-250 ◽  
Author(s):  
C. H. Yang ◽  
B. K. Kenduiywo ◽  
U. Soergel
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Ground Truth ◽  
Google Earth ◽  
Aerial Images ◽  
Radar Signal ◽  
Persistent Scatterer Interferometry ◽  
Small Surface ◽  
Persistent Scatterer ◽  
Main Station

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.

scholarly journals CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – CASE STUDY OF MONITORING AN URBAN AREA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 123-130 ◽  
Author(s):  
C. H. Yang ◽  
U. Soergel
Keyword(s):  
Change Detection ◽  
Surface Deformation ◽  
Regular Structure ◽  
Test Site ◽  
Radar Signal ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
Multi Temporal ◽  
Local Comparison

Persistent Scatterer Interferometry (PSI) is a technique to extract subtle surface deformation from sets of scatterers identified in time-series of SAR images which feature temporally stable and strong radar signal (i.e., Persistent Scatterers, PS). Because of the preferred rectangular and regular structure of man-made objects, PSI works particularly well for monitoring of settlements. Usually, in PSI it is assumed that except for surface motion the scene is steady. In case this is not given, corresponding PS candidates are discarded during PSI processing. On the other hand, pixel-based change detection relying on local comparison of multi-temporal images typically highlights scene modifications of larger size rather than detail level. <br><br> In this paper, we propose a method to combine these two types of change detection approaches. First, we introduce a local change-index based on PSI, which basically looks for PS candidates that remain stable over a certain period of time, but then break down suddenly. In addition, for the remaining PS candidates we apply common PSI processing which yields attributes like velocity in line-of-sight. In order to consider context, we apply now spatial filtering according to the derived attributes and morphology to exclude outliers and extract connect components of similar regions at the same time. We demonstrate our approach for test site Berlin, Germany, where, firstly, deformation-velocities on man-made structures are estimated and, secondly, some construction-sites are correctly recognized.

scholarly journals EVALUATION OF A PSI-BASED CHANGE DETECTION REGARDING SIMULATION, COMPARISON, AND APPLICATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1959-1965
Author(s):  
C. H. Yang ◽  
U. Soergel
Keyword(s):  
Change Detection ◽  
Ground Truth ◽  
Coherent Signals ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterer ◽  
Deformation Velocity ◽  
Potential Applications ◽  
Pros And Cons ◽  
Coherent Radar ◽  
Occurrence Times

<p><strong>Abstract.</strong> Persistent scatterer interferometry (PSI) detects and analyses strong, stable, and coherent radar signals throughout a time series of SAR images. Such coherent signals are reflected from corner-reflector-like substructures in built-up cities, which are regarded as so-called PS points. Certain PS properties such as deformation velocity and topography height can be derived for scene monitoring. Previously, we introduced a PSI-based change detection to detect disappearing and emerging PS points along with their occurrence times. Such temporary PS points existing only during a certain period correspond to change events, e.g., mostly constructions in cities. The tests using TerraSAR-X images successfully identified where and when the construction events in Berlin took place in 2013. The results were compared and all agreed with the ground truth. In this study, we evaluate our method more deeply. A simulation test is conducted to evaluate the theoretical accuracy in space and time. We also compare our method with two classical approaches: image rationing and amplitude-based semi-PS detection. The computational requirements are revealed afterwards. Finally, potential applications are proposed and discussed. All of these works help us to better characterize our technique and learn the pros and cons.</p>

scholarly journals Semi-Automatic Identification and Pre-Screening of Geological–Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets

2019 ◽  
Vol 11 (14) ◽  
pp. 1675 ◽  
Author(s):  
Tomás ◽  
Pagán ◽  
Navarro ◽  
Cano ◽  
Pastor ◽  
...  
Keyword(s):  
Regional Scale ◽  
Central Italy ◽  
Geographical Information ◽  
External Information ◽  
Automatic Identification ◽  
Persistent Scatterer Interferometry ◽  
Active Deformation ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
Map Scale

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima–Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5,000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale.

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