Efficient ground surface displacement monitoring using Sentinel-1 data: Integrating distributed scatterers (DS) identified using two-sample t-test with persistent scatterers (PS)

Download statistics - Document (COUNTER):

Shamshiri, R.; Nahavandchi, H.; Motagh, M.; Hooper, A.: Efficient ground surface displacement monitoring using Sentinel-1 data: Integrating distributed scatterers (DS) identified using two-sample t-test with persistent scatterers (PS). In: Remote Sensing 10 (2018), Nr. 5, 794. DOI: https://doi.org/10.3390/rs10050794

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/3470

Selected time period:


Sum total of downloads: 90

Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov-Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of images. © 2018 by the authors.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 55 61.11%
2 image of flag of United States United States 10 11.11%
3 image of flag of China China 5 5.56%
4 image of flag of Korea, Republic of Korea, Republic of 3 3.33%
5 image of flag of No geo information available No geo information available 2 2.22%
6 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 2 2.22%
7 image of flag of Canada Canada 2 2.22%
8 image of flag of Slovenia Slovenia 1 1.11%
9 image of flag of Russian Federation Russian Federation 1 1.11%
10 image of flag of Malaysia Malaysia 1 1.11%
    other countries 8 8.89%

Further download figures and rankings:


Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository