A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series Article Swipe
Mehdi Khaki
,
M. S. Filmer
,
W. E. Featherstone
,
Michael Kühn
,
Luyen K. Bui
,
Amy Parker
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tgrs.2019.2950353
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tgrs.2019.2950353
This article proposes an alternative filtering technique to improve interferometric synthetic aperture radar (InSAR) time series by reducing residual noise while retaining the ground deformation signal. To this end, for the first time, a data-driven approach is introduced, which is based on Takens's method within the sequential Monte Carlo framework, allowing for a model-free approach to filter noisy data. Both a Kalman-based filter and a particle filter (PF) are applied within this framework to investigate their impact on retrieving the signals. More specifically, PF and particle smoother [PaSm; to avoid confusion with persistent scatterers (PSs)] are tested for their ability to deal with non-Gaussian noise. A synthetic test based on simulated InSAR time series, as well as a real test, is designed to investigate the capability of the proposed approach compared with the spatiotemporal filtering of InSAR time series. Results indicate that PFs and more specifically PaSm perform better than other applied methods, as indicated by reduced errors in both tests. Two other variants of PF and adaptive unscented Kalman filter (AUKF) are presented and are found to be able to perform similar to PaSm but with reduced computation time. This article suggests that PFs tested here could be applied in InSAR processing chains.
Related Topics
Concepts
Computer science
Interferometric synthetic aperture radar
Kalman filter
Algorithm
Particle filter
Noise (video)
Monte Carlo method
Synthetic aperture radar
Filter (signal processing)
Series (stratigraphy)
Artificial intelligence
Computer vision
Mathematics
Statistics
Geology
Image (mathematics)
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tgrs.2019.2950353
- OA Status
- green
- Cited By
- 20
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2990090712
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2990090712Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tgrs.2019.2950353Digital Object Identifier
- Title
-
A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time SeriesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-11-26Full publication date if available
- Authors
-
Mehdi Khaki, M. S. Filmer, W. E. Featherstone, Michael Kühn, Luyen K. Bui, Amy ParkerList of authors in order
- Landing page
-
https://doi.org/10.1109/tgrs.2019.2950353Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://espace.curtin.edu.au/bitstream/20.500.11937/81729/3/81772.pdfDirect OA link when available
- Concepts
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Computer science, Interferometric synthetic aperture radar, Kalman filter, Algorithm, Particle filter, Noise (video), Monte Carlo method, Synthetic aperture radar, Filter (signal processing), Series (stratigraphy), Artificial intelligence, Computer vision, Mathematics, Statistics, Geology, Image (mathematics), PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2, 2023: 2, 2022: 5, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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