Automatic Insar Phase Modeling and Quality Assessment Using Machine Learning and Hypothesis Testing

Bas van de Kerkhof, Victor Pankratius, Ling Chang, Rob van Swol, Ramon Hanssen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.
Original languageEnglish
Title of host publicationIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
EditorsJose Moreno
PublisherIEEE
Pages4427-4430
Volume2018
ISBN (Electronic)978-1-5386-7150-4
ISBN (Print)978-1-5386-7151-1
DOIs
Publication statusPublished - 2018
EventIGARSS 2018: 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38
https://www.igarss2018.org/

Conference

ConferenceIGARSS 2018: 2018 IEEE International Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18
Internet address

Keywords

  • InSAR
  • machine learning
  • hypothesis testing
  • stochastics

Fingerprint Dive into the research topics of 'Automatic Insar Phase Modeling and Quality Assessment Using Machine Learning and Hypothesis Testing'. Together they form a unique fingerprint.

Cite this