Phase unwrapping (PU) is one of the key processing steps in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from observation of its noisy principal (wrapped) values. Recently, PU approaches based on Kalman filtering have proved their efficacy in tackling the PU problem even when strong discontinuities of the height profile and noisy data are involved. This paper presents a novel multichannel InSAR PU algorithm using several interferometric SAR images based on the extended Kalman filter. The proposed technique exploits the capability of the Kalman algorithm to simultaneously perform noise filtering, PU, and multi-sensor data fusion. The proposed method, even being a Bayesian estimator, optimally fuses height information coming from an additional maximum likelihood estimator (MLE) combining the benefits of both the Bayesian and the non-Bayesian approaches. The performance of the proposed algorithm has been tested on simulated interferometric images proving the effectiveness of the proposed method.

Multichannel interferometric SAR phase unwrapping using extended Kalman Smoother

SCHIRINZI, Gilda
2013

Abstract

Phase unwrapping (PU) is one of the key processing steps in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from observation of its noisy principal (wrapped) values. Recently, PU approaches based on Kalman filtering have proved their efficacy in tackling the PU problem even when strong discontinuities of the height profile and noisy data are involved. This paper presents a novel multichannel InSAR PU algorithm using several interferometric SAR images based on the extended Kalman filter. The proposed technique exploits the capability of the Kalman algorithm to simultaneously perform noise filtering, PU, and multi-sensor data fusion. The proposed method, even being a Bayesian estimator, optimally fuses height information coming from an additional maximum likelihood estimator (MLE) combining the benefits of both the Bayesian and the non-Bayesian approaches. The performance of the proposed algorithm has been tested on simulated interferometric images proving the effectiveness of the proposed method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/29077
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