| Title | Pair selection optimization for InSAR time series processing |
| |
| Author | Smittarello, D ;
d'Oreye, N; Jaspard, M; Derauw, D; Samsonov, S |
| Source | Journal of Geophysical Research, Solid Earth vol. 127, issue 3, 2022 p. 1-24, https://doi.org/10.1029/2021JB022825  Open Access |
| Image |  |
| Year | 2022 |
| Alt Series | Natural Resources Canada, Contribution Series 20220055 |
| Publisher | AGU |
| Document | serial |
| Lang. | English |
| Media | paper; digital; on-line |
| File format | pdf |
| Area | Réunion; Argentina; Congo, the Democratic Republic of the |
| Lat/Long WENS | -70.4167 -70.3833 -36.5667 -36.5833 |
| Lat/Long WENS | 29.5167 29.5167 -0.5000 -1.0000 |
| Lat/Long WENS | 55.5333 55.5333 -21.1000 -21.1167 |
| Subjects | mathematical and computational geology; SARS; satellites; remote sensing; displacement; software; synthetic aperture radar surveys (SAR) |
| Illustrations | location maps; plots; tables; time series; digital elevation models |
| Program | Canada Centre for Remote Sensing People Support and Leadership |
| Released | 2022 03 01 |
| Abstract | The ever-increasing amount of Synthetic Aperture Radar (SAR) data motivates the development of automatic processing chains to fully exploit the opportunities offered by these large databases. The
Synthetic Aperture Radar Interferometry (InSAR) Mass processing Toolbox for Multidimensional time series is an optimized tool to automatically download SAR data, select the interferometric pairs, perform the interferometric mass processing, compute
the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is available. New challenges relate to data management and processing load. We address them through
methodological improvements dedicated to optimizing the InSAR pair selection. The proposed algorithm narrows the classical selection based on the shortest temporal and spatial baselines thanks to a coherence proxy and balances the use of each image
as Primary and Secondary images thanks to graph theory methods. We apply the processing to three volcanic areas characterized with different climate, vegetation, and deformation characteristics: the Virunga Volcanic Province (DR Congo), the Reunion
Island (France), and the Domuyo and Laguna del Maule area (Chile-Argentina border). Compared to pair selection based solely on baseline criteria, this new tool produces similar velocity maps while reducing the total number of computed differential
InSAR interferograms by up to 75%, which drastically reduces the computation time. The optimization also allows to reduce the influence of DEM errors and atmospheric phase screen, which increase the signal-to-noise ratio of the inverted displacement
time series. |
| Summary | (Plain Language Summary, not published)
Development of satellite remote sensing greatly helps to mitigate natural hazard in remote or dangerous areas like volcano-tectonic regions or
landslide-prone regions. In particular, Synthetic Aperture Radar Interferometry (InSAR) offers the possibility to measure ground surface displacements with millimeter resolution. Several methods exist to benefit from the large amount of data to
perform time series of ground deformation with sub-centimeter resolution. However, the ever-increasing number of available images poses new challenges (e.g., to process the large amount of data, to manage large databases and to extract useful
information in near-real time for operative purposes). Mass processing Toolbox for Multidimensional time series (MasTer) is a fully automatic tool able to provide updated velocity maps and displacement time series resulting from the processing of
satellites radar images, which are regularly acquired by space agencies. Hereby, we present a methodological development to speed up the processing and improve the signal-to-noise ratio of the obtained ground deformation time series. This is achieved
by optimizing the InSAR pair selection. By also reducing the storage space and raw-memory requirements, it allows processing longer time series with the same computational infrastructure. The proposed algorithm, written in Python, is included in the
MasTer toolbox, though it can easily be adapted for other time series software. |
| GEOSCAN ID | 330039 |
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