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 language | English |
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Title of host publication | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium |
Editors | Jose Moreno |
Publisher | IEEE |
Pages | 4427-4430 |
Volume | 2018 |
ISBN (Electronic) | 978-1-5386-7150-4 |
ISBN (Print) | 978-1-5386-7151-1 |
DOIs | |
Publication status | Published - 2018 |
Event | IGARSS 2018: 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/ |
Conference
Conference | IGARSS 2018: 2018 IEEE International Geoscience and Remote Sensing Symposium |
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Abbreviated title | IGARSS 2018 |
Country/Territory | Spain |
City | Valencia |
Period | 22/07/18 → 27/07/18 |
Internet address |
Keywords
- InSAR
- machine learning
- hypothesis testing
- stochastics