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

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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

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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

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pos. country downloads
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1 image of flag of Germany Germany 29 64.44%
2 image of flag of Korea, Republic of Korea, Republic of 3 6.67%
3 image of flag of China China 3 6.67%
4 image of flag of No geo information available No geo information available 2 4.44%
5 image of flag of Slovenia Slovenia 1 2.22%
6 image of flag of Russian Federation Russian Federation 1 2.22%
7 image of flag of Malaysia Malaysia 1 2.22%
8 image of flag of India India 1 2.22%
9 image of flag of Switzerland Switzerland 1 2.22%
10 image of flag of Belarus Belarus 1 2.22%
    other countries 2 4.44%

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