By Topic

A Wavelet-Based Multitemporal DInSAR Algorithm for Monitoring Ground Surface Motion

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Manoochehr Shirzaei ; Berkeley Seismological Laboratory, University of California, Berkeley, CA, USA

I present a multitemporal algorithm with an improved filtering scheme compared with earlier works that combines and inverts a large set of unwrapped interferograms to generate an accurate time series of the surface motion. This method statistically analyzes the interferometric phase noise to identify stable pixels. Then, it applies an iterative 2-D sparse phase unwrapping operator and low-pass filter to each interferogram to obtain reliable absolute phase changes. Moreover, it uses a re-weighted least squares approach to robustly estimate the time series of the surface motion, which is followed by a temporal low-pass filter that reduces the effects of atmospheric delay. During various stages of the analysis, this approach applies a variety of sophisticated wavelet-based filters to estimate the interferometric phase noise and to reduce the effects of systematic and random artefacts, such as spatially correlated and temporally uncorrelated components of the atmospheric delay, and the digital elevation model and orbital errors. To demonstrate the capability of this method for accurately measuring nonlinear surface motions, I analyze a large set of SAR data acquired by the ENVISAT satellite from 2003 through 2010 over the south flank of the Kilauea volcano, Hawaii. The validation test shows that my approach is able to retrieve the surface displacement with an average accuracy of 6.5 mm.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:10 ,  Issue: 3 )