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 F. Hanssen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-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 publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Place of PublicationValencia
PublisherIEEE
Pages4427-4430
Number of pages4
ISBN (Electronic)9781538671504
DOIs
Publication statusPublished - 31 Oct 2018
Externally publishedYes
Event38th IEEE International Geoscience and Remote Sensing Symposium 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38
https://www.igarss2018.org/

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th IEEE International Geoscience and Remote Sensing Symposium 2018
Abbreviated titleIGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18
Internet address

Keywords

  • Hypothesis testing
  • InSAR
  • Machine learning
  • Stochastics

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  • Cite this

    Van De Kerkhof, B., Pankratius, V., Chang, L., Van Swol, R., & Hanssen, R. F. (2018). Automatic InSAR phase modeling and quality assessment using machine learning and hypothesis testing. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 4427-4430). [8518460] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Valencia: IEEE. https://doi.org/10.1109/IGARSS.2018.8518460