dc.identifier.uri |
http://dx.doi.org/10.15488/3470 |
|
dc.identifier.uri |
http://www.repo.uni-hannover.de/handle/123456789/3500 |
|
dc.contributor.author |
Shamshiri, Roghayeh
|
|
dc.contributor.author |
Nahavandchi, Hossein
|
|
dc.contributor.author |
Motagh, Mahdi
|
|
dc.contributor.author |
Hooper, Andy
|
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dc.date.accessioned |
2018-06-13T13:18:48Z |
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dc.date.available |
2018-06-13T13:18:48Z |
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dc.date.issued |
2018 |
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dc.identifier.citation |
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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dc.description.abstract |
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. |
eng |
dc.language.iso |
eng |
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dc.publisher |
Basel : MDPI AG |
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dc.relation.ispartofseries |
Remote Sensing 10 (2018), Nr. 5 |
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dc.rights |
CC BY 4.0 Unported |
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dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
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dc.subject |
Distributed scatterer |
eng |
dc.subject |
Persistent scatterer |
eng |
dc.subject |
Sentinel-1 |
eng |
dc.subject |
SqueeSAR |
eng |
dc.subject |
StaMPS/MTI |
eng |
dc.subject |
T-test |
eng |
dc.subject |
Indium compounds |
eng |
dc.subject |
Pixels |
eng |
dc.subject |
Signal processing |
eng |
dc.subject |
Testing |
eng |
dc.subject |
Time series analysis |
eng |
dc.subject |
Distributed scatterers |
eng |
dc.subject |
Persistent scatterers |
eng |
dc.subject |
Sentinel-1 |
eng |
dc.subject |
SqueeSAR |
eng |
dc.subject |
T-tests |
eng |
dc.subject |
Synthetic aperture radar |
eng |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
ger |
dc.title |
Efficient ground surface displacement monitoring using Sentinel-1 data: Integrating distributed scatterers (DS) identified using two-sample t-test with persistent scatterers (PS) |
|
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.issn |
2072-4292 |
|
dc.relation.doi |
https://doi.org/10.3390/rs10050794 |
|
dc.bibliographicCitation.issue |
5 |
|
dc.bibliographicCitation.volume |
10 |
|
dc.bibliographicCitation.firstPage |
794 |
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dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
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