Spatiotemporal Filling of Missing Data in Remotely Sensed Displacement Measurement Time Series
| Autor: | Alexandre Hippert-Ferrer, Philippe Bolon, Romain Millan, Yajing Yan |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| Rok vydání: | 2021 |
| Předmět: |
Series (mathematics)
Cross-correlation Computer science 0206 medical engineering Orthogonal functions 02 engineering and technology Covariance Geotechnical Engineering and Engineering Geology Missing data 020601 biomedical engineering Displacement (vector) 03 medical and health sciences 0302 clinical medicine [SDE]Environmental Sciences Convergence (routing) Noise (video) Electrical and Electronic Engineering Algorithm ComputingMilieux_MISCELLANEOUS 030217 neurology & neurosurgery |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2021, 18 (12), pp.2157-2161. ⟨10.1109/LGRS.2020.3015149⟩ |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2020.3015149 |
| Popis: | Missing data is a critical pitfall in the investigation of remotely sensed displacement measurement because it prevents from a full understanding of the physical phenomenon under observation. In the sight of reconstructing incomplete displacement data, this letter presents a data-driven spatiotemporal gap-filling method, which is an extension of the expectation-maximization-empirical orthogonal function (EM-EOF) method. The presented method decomposes an augmented spatiotemporal covariance of a displacement time series into EOF modes and then selects the optimal set of EOF modes to reconstruct the time series. This selection is based on the cross-validation root-mean-square error and a confidence index associated with each eigenvalue. The estimated missing values are then iteratively updated until convergence. Results on displacement time series derived from cross correlation of Sentinel-2 optical images over Fox Glacier in New-Zealand's Alps show that the reconstruction accuracy is improved compared with the EM-EOF method. The proposed extension can tackle challenging cases, i.e., short time series with heterogeneous displacement behaviors corrupted by a large amount of missing data and noise. |
| Databáze: | OpenAIRE |
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