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.
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License of this version: | CC BY 4.0 Unported - https://creativecommons.org/licenses/by/4.0/ |
Publication type: | Article |
Publishing status: | publishedVersion |
Publication date: | 2018 |
Keywords english: | Distributed scatterer, Persistent scatterer, Sentinel-1, SqueeSAR, StaMPS/MTI, T-test, Indium compounds, Pixels, Signal processing, Testing, Time series analysis, Distributed scatterers, Persistent scatterers, Sentinel-1, SqueeSAR, T-tests, Synthetic aperture radar |
DDC: | 620 | Ingenieurwissenschaften und Maschinenbau |
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