A Stochastic Model for InSAR Timeseries

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A Stochastic Model for InSAR Timeseries

Estimation and Propagation for Reduced Datasets

Conference Paper (2021)

Author(s)

Sami Samiei-Esfahany (University of Tehran, TU Delft - Civil Engineering & Geosciences)

Freek J. van Leijen (TU Delft - Civil Engineering & Geosciences)

Ramon F. Hanssen (TU Delft - Civil Engineering & Geosciences)

Research Group

Mathematical Geodesy and Positioning

InSAR Stochastic model Radar interferometry Data reduction Noise model

DOI related publication

https://doi.org/10.1109/IGARSS47720.2021.9553309 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:4c6885a7-e990-4a05-92b4-059292a62cec

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Publication Year

2021

Language

English

Research Group

Mathematical Geodesy and Positioning

Article number

9553309

Pages (from-to)

3185-3188

ISBN (print)

978-1-6654-4762-1

ISBN (electronic)

978-1-6654-0369-6

Event

IGARSS 2021 (2021-07-11 - 2021-07-16), Virtual at Brussels, Belgium

Downloads counter

185

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.