DocumentCode :
677542
Title :
New algorithm for InSAR stack phase triangulation using integer least squares estimation
Author :
Samiei-Esfahany, Sami ; Hanssen, Ramon F.
Author_Institution :
Dept. of Geosci. & Remote Sensing, Delft Univ. of Technol., Delft, Netherlands
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
884
Lastpage :
887
Abstract :
Algorithms have been proposed in the recent years in order to retrieve all information available in interferometric stacks of SAR acquisitions with focus on distributed scatterers. One of the key steps in these algorithms - called phase triangulation, phase linking or phase multi-linking - is to optimally estimate filtered wrapped interferometric phases from all possible interferometric combinations preserving useful information and filtering noise. The advantages of these methods compared to conventional approaches are that the algorithm can be applied before phase unwrapping, and that it considers all possible interferograms. In this contribution we propose a new algorithm for phase triangulation based on the integer least squares (ILS) method. We model the phase triangulation problem as a system of linear observation equations. After computing the full covariance matrix of interferometric phases using a Monte-Carlo method, we use ILS to estimate the unknowns. The advantages of our method are that it is capable of considering the mutual correlation between all interferograms, and additionally provides as a output the precision of the estimates. Simulation results show that the proposed method works effectively and can optimally filter noise from interferometric stacks before unwrapping.
Keywords :
Monte Carlo methods; covariance matrices; geophysics computing; least squares approximations; radar interferometry; remote sensing by radar; synthetic aperture radar; InSAR stack phase triangulation; Monte-Carlo method; full covariance matrix; integer least squares estimation; interferometric synthetic aperture radar; linear observation equations; phase unwrapping; Covariance matrices; Decorrelation; Mathematical model; Noise; Standards; Time series analysis; Vectors; InSAR; Integer Least Squares estimation; coherence matrix; distributed scatterers (DS);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6721301
Filename :
6721301
Link To Document :
بازگشت