Sequential estimator: A novel approach for efficient high-precision analysis of interferometric time series

Homa Ansari, Francesco De Zan, Richard Bamler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Wide-swath satellite missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric time series and open new opportunities for systematic monitoring of the Earth surface. The processing of the emerging Big Data with the state-of-the-art InSAR time series analysis techniques is, however, challenging. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from the long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. This scheme avoids the necessity of re-processing the entire data stack at the face of each new acquisition. It is shown that the proposed estimator introduces negligible degradation compared to the Cramér-Rao Lower Bound. The estimator may therefore be adapted for high-precision Near-Real-Time processing of InSAR and accommodate the conversion of InSAR from an off-line to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to the state-of-the-art techniques via simulations and application to Sentinel-1 data.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages980-983
Number of pages4
ISBN (Electronic)9781509049516
DOIs
StatePublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth
Period23/07/1728/07/17

Keywords

  • Big Data
  • Differential InSAR
  • Time Series

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