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

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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 languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Subtitle of host publicationProceedings
Place of PublicationDanvers
PublisherIEEE
Pages3185-3188
Number of pages4
ISBN (Electronic)978-1-6654-0369-6
ISBN (Print)978-1-6654-4762-1
DOIs
Publication statusPublished - 2021
EventIGARSS 2021: 2021 IEEE International Geoscience and Remote Sensing Symposium - Virtual at Brussels, Belgium
Duration: 11 Jul 202116 Jul 2021

Conference

ConferenceIGARSS 2021
CountryBelgium
CityVirtual at Brussels
Period11/07/2116/07/21

Keywords

  • radar interferometry
  • InSAR
  • stochastic model
  • noise model
  • data reduction

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