A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

This article proposes an alternative filtering technique to improve interferometric synthetic aperture radar (InSAR) time series by reducing residual noise while retaining the ground deformation signal. To this end, for the first time, a data-driven approach is introduced, which is based on Takens&#...

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Bibliographic Details
Main Authors: Khaki, Mehdi, Filmer, Mick, Featherstone, Will, Kuhn, Michael, Bui, Khac Luyen, Parker, Amy
Format: Journal Article
Language:English
Published: IEEE 2019
Subjects:
Science & Technology
Physical Sciences
Technology
Geochemistry & Geophysics
Engineering, Electrical & Electronic
Remote Sensing
Imaging Science & Photographic Technology
Engineering
Time series analysis
Strain
Delays
Monte Carlo methods
Kalman filters
Mathematical model
Training data
Data-driven technique
interferometric synthetic aperture radar (InSAR)
non-Gaussian noise
particle filter (PF)
sequential technique
SURFACE DEFORMATION
SAR
INTERFEROMETRY
Online Access:http://hdl.handle.net/20.500.11937/81729

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