On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series

Wietske S. Brouwer; Yuqing Wang; Freek J. Van Leijen; Ramon F. Hanssen

doi:10.1109/IGARSS52108.2023.10282629

On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series

Wietske S. Brouwer^*, Yuqing Wang, Freek J. Van Leijen, Ramon F. Hanssen

^*Corresponding author for this work

Mathematical Geodesy and Positioning

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

InSAR enables the estimation of displacements of (objects on) the earth's surface. To provide reliable estimates, both a stochastic and mathematical model are required. However, the intrinsic problem of InSAR is that both are unknown. Here we derive the Variance-Covariance Matrix (VCM) for double differenced phase observations for an arc, i.e., the phase difference between two points relative to a reference epoch. Using the Normalized Amplitude Dispersion we subdivide the time series in multiple partitions. The method results in a more realistic stochastic model, and consequently more realistic and reliable displacement parameters. The stochastic model also allows to make statements on the precision and reliability of the estimated parameters.

Original language	English
Title of host publication	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	IEEE
Pages	7902-7905
Number of pages	4
ISBN (Electronic)	9798350320107
DOIs	https://doi.org/10.1109/IGARSS52108.2023.10282629
Publication status	Published - 2023
Event	2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023) - Pasadena Convention Center, Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 https://2023.ieeeigarss.org/

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2023-July

Conference

Conference	2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023)
Abbreviated title	IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/23 → 21/07/23
Internet address	https://2023.ieeeigarss.org/

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

InSAR
parameter estimation
Point Scatterers
stochastic model

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10.1109/IGARSS52108.2023.10282629

On the Stochastic Model for InSAR Single Arc Point Scatterer Time SeriesFinal published version, 1.97 MB

Cite this

Brouwer, W. S., Wang, Y., Van Leijen, F. J., & Hanssen, R. F. (2023). On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 7902-7905). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10282629

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title = "On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series",

abstract = "InSAR enables the estimation of displacements of (objects on) the earth's surface. To provide reliable estimates, both a stochastic and mathematical model are required. However, the intrinsic problem of InSAR is that both are unknown. Here we derive the Variance-Covariance Matrix (VCM) for double differenced phase observations for an arc, i.e., the phase difference between two points relative to a reference epoch. Using the Normalized Amplitude Dispersion we subdivide the time series in multiple partitions. The method results in a more realistic stochastic model, and consequently more realistic and reliable displacement parameters. The stochastic model also allows to make statements on the precision and reliability of the estimated parameters.",

keywords = "InSAR, parameter estimation, Point Scatterers, stochastic model",

author = "Brouwer, {Wietske S.} and Yuqing Wang and {Van Leijen}, {Freek J.} and Hanssen, {Ramon F.}",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023), IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",

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doi = "10.1109/IGARSS52108.2023.10282629",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "IEEE",

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Brouwer, WS , Wang, Y , Van Leijen, FJ & Hanssen, RF 2023, On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, IEEE, pp. 7902-7905, 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023), Pasadena, California, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282629

On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series. / Brouwer, Wietske S.; Wang, Yuqing ; Van Leijen, Freek J. et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. IEEE, 2023. p. 7902-7905 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Van Leijen, Freek J.

AU - Hanssen, Ramon F.

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

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N2 - InSAR enables the estimation of displacements of (objects on) the earth's surface. To provide reliable estimates, both a stochastic and mathematical model are required. However, the intrinsic problem of InSAR is that both are unknown. Here we derive the Variance-Covariance Matrix (VCM) for double differenced phase observations for an arc, i.e., the phase difference between two points relative to a reference epoch. Using the Normalized Amplitude Dispersion we subdivide the time series in multiple partitions. The method results in a more realistic stochastic model, and consequently more realistic and reliable displacement parameters. The stochastic model also allows to make statements on the precision and reliability of the estimated parameters.

AB - InSAR enables the estimation of displacements of (objects on) the earth's surface. To provide reliable estimates, both a stochastic and mathematical model are required. However, the intrinsic problem of InSAR is that both are unknown. Here we derive the Variance-Covariance Matrix (VCM) for double differenced phase observations for an arc, i.e., the phase difference between two points relative to a reference epoch. Using the Normalized Amplitude Dispersion we subdivide the time series in multiple partitions. The method results in a more realistic stochastic model, and consequently more realistic and reliable displacement parameters. The stochastic model also allows to make statements on the precision and reliability of the estimated parameters.

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ER -

On the Stochastic Model for InSAR Single Arc Point Scatterer Time Series

Abstract

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Keywords

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