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    A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

    81772.pdf (4.384Mb)
    Access Status
    Open access
    Authors
    Khaki, Mehdi
    Filmer, Mick
    Featherstone, Will
    Kuhn, Michael
    Bui, Khac Luyen
    Parker, Amy
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Khaki, M. and Filmer, M. and Featherstone, W. and Kuhn, M. and Bui, K.L. and Parker, A. 2019. A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series. IEEE Transactions on Geoscience and Remote Sensing. 58 (3): pp. 1904-1912.
    Source Title
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    10.1109/TGRS.2019.2950353
    ISSN
    0196-2892
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    Remarks

    Copyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in otherworks.

    URI
    http://hdl.handle.net/20.500.11937/81729
    Collection
    • Curtin Research Publications
    Abstract

    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.

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