A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets

Sami Samiei-Esfahany; Freek J. van Leijen; Ramon F. Hanssen

doi:10.1109/IGARSS47720.2021.9553309

A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets

Sami Samiei-Esfahany, Freek J. van Leijen, Ramon F. Hanssen

Mathematical Geodesy and Positioning

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

Abstract

The main objective of this paper is to develop and evaluate a pragmatic approach to obtain an InSAR stochastic model for reduced InSAR datasets. This goal is achieved by calculation of the stochastic parameters per InSAR stack, propagating the noise structure to reduced datasets. The propagation of full covariance matrices when using a reduced dataset in space and time is avoided, using the derived analytical functions. This way, a computationally efficient approximation of the exact covariance matrix is obtained for reduced datasets.

Original language	English
Title of host publication	2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Subtitle of host publication	Proceedings
Place of Publication	Danvers
Publisher	IEEE
Pages	3185-3188
Number of pages	4
ISBN (Electronic)	978-1-6654-0369-6
ISBN (Print)	978-1-6654-4762-1
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9553309
Publication status	Published - 2021
Event	IGARSS 2021: 2021 IEEE International Geoscience and Remote Sensing Symposium - Virtual at Brussels, Belgium Duration: 11 Jul 2021 → 16 Jul 2021

Conference

Conference	IGARSS 2021
Country/Territory	Belgium
City	Virtual at Brussels
Period	11/07/21 → 16/07/21

Keywords

radar interferometry
InSAR
stochastic model
noise model
data reduction

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10.1109/IGARSS47720.2021.9553309

Cite this

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title = "A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets",

abstract = "The main objective of this paper is to develop and evaluate a pragmatic approach to obtain an InSAR stochastic model for reduced InSAR datasets. This goal is achieved by calculation of the stochastic parameters per InSAR stack, propagating the noise structure to reduced datasets. The propagation of full covariance matrices when using a reduced dataset in space and time is avoided, using the derived analytical functions. This way, a computationally efficient approximation of the exact covariance matrix is obtained for reduced datasets.",

keywords = "radar interferometry, InSAR, stochastic model, noise model, data reduction",

author = "Sami Samiei-Esfahany and {van Leijen}, {Freek J.} and Hanssen, {Ramon F.}",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9553309",

language = "English",

isbn = "978-1-6654-4762-1",

pages = "3185--3188",

booktitle = "2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS",

publisher = "IEEE",

address = "United States",

note = "IGARSS 2021 : 2021 IEEE International Geoscience and Remote Sensing Symposium ; Conference date: 11-07-2021 Through 16-07-2021",

}

Samiei-Esfahany, S, van Leijen, FJ & Hanssen, RF 2021, A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets. in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS: Proceedings., 9553309, IEEE, Danvers, pp. 3185-3188, IGARSS 2021, Virtual at Brussels, Belgium, 11/07/21. https://doi.org/10.1109/IGARSS47720.2021.9553309

A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets. / Samiei-Esfahany, Sami; van Leijen, Freek J.; Hanssen, Ramon F.
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS: Proceedings. Danvers: IEEE, 2021. p. 3185-3188 9553309.

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

TY - GEN

T1 - A Stochastic Model for InSAR Timeseries

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AU - Samiei-Esfahany, Sami

AU - van Leijen, Freek J.

AU - Hanssen, Ramon F.

PY - 2021

Y1 - 2021

N2 - The main objective of this paper is to develop and evaluate a pragmatic approach to obtain an InSAR stochastic model for reduced InSAR datasets. This goal is achieved by calculation of the stochastic parameters per InSAR stack, propagating the noise structure to reduced datasets. The propagation of full covariance matrices when using a reduced dataset in space and time is avoided, using the derived analytical functions. This way, a computationally efficient approximation of the exact covariance matrix is obtained for reduced datasets.

AB - The main objective of this paper is to develop and evaluate a pragmatic approach to obtain an InSAR stochastic model for reduced InSAR datasets. This goal is achieved by calculation of the stochastic parameters per InSAR stack, propagating the noise structure to reduced datasets. The propagation of full covariance matrices when using a reduced dataset in space and time is avoided, using the derived analytical functions. This way, a computationally efficient approximation of the exact covariance matrix is obtained for reduced datasets.

KW - radar interferometry

KW - InSAR

KW - stochastic model

KW - noise model

KW - data reduction

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U2 - 10.1109/IGARSS47720.2021.9553309

DO - 10.1109/IGARSS47720.2021.9553309

M3 - Conference contribution

SN - 978-1-6654-4762-1

SP - 3185

EP - 3188

BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

PB - IEEE

CY - Danvers

Y2 - 11 July 2021 through 16 July 2021

ER -

A Stochastic Model for InSAR Timeseries: Estimation and Propagation for Reduced Datasets

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Keywords

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