Functional model selection for InSAR time series

Ling Chang, Ramon F. Hanssen

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

InSAR time series analysis involves the processing of extremely large datasets to estimate the relative movements of points on Earth. The estimated movements may reveal geophysical processes, or strain in anthropogenic structures. In parametric estimation methods, it is important to chose the optimal mathematical functional model relating the satellite observations to the kinematic parameters of interest. A standard approach is to parameterize the kinematic behavior, in first order, as a linear function of time, but it is unlikely that all objects behave in this purely linear way. Ideally, the kinematic parameterization should be optimized for each individual measurement point in the area of interest. In this work, following [1] we introduce a method to select the optimal functional model, with a minimum but sufficient number of free parameters using a probabilistic method based on multiple hypotheses testing.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherIEEE
Pages3390-3393
Number of pages4
Volume2016-November
ISBN (Electronic)9781509033324
DOIs
Publication statusPublished - 1 Nov 2016
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016: Advancing the Understanding of Our Living Planet - Beijing, China
Duration: 10 Jul 201615 Jul 2016
http://www.igarss2016.org/

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Abbreviated titleIGARSS
CountryChina
CityBeijing
Period10/07/1615/07/16
Internet address

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Keywords

  • and the B-method of testing
  • Deformation modeling
  • Multiple hypotheses testing

Cite this

Chang, L., & Hanssen, R. F. (2016). Functional model selection for InSAR time series. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (Vol. 2016-November, pp. 3390-3393). [7729876] IEEE. https://doi.org/10.1109/IGARSS.2016.7729876