A Stochastic Model for InSAR Timeseries

Estimation and Propagation for Reduced Datasets

Conference paper (2021)

Authors

S. Samiei Esfahany University of Tehran, Mathematical Geodesy and Positioning -

F.J. van Leijen Mathematical Geodesy and Positioning -

R.F. Hanssen Mathematical Geodesy and Positioning -

Research Group

Mathematical Geodesy and Positioning () (TU Delft)

InSAR Stochastic model Radar interferometry Data reduction Noise model

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

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Published Date

2021

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Mathematical Geodesy and Positioning

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.

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