Within this manuscript a novel technique for joint Digital Elevation Model (DEM) reconstruction and deformation estimation is presented. In particular, a Maximum A Posteriori (MAP) estimator that makes use of Gaussian Markov Random Fields (MRF) is proposed. The advantage of the approach, with respect to classical Permanent Scatterers (PS) based techniques, consists of its ability to evaluate the height and the deformation for all resolution cell across the scene, instead of only strong scatterers. Thus, the method is able to work also in natural scenarios, or in general when few PS are available. First results are presented on a simulated dataset with COSMO-SkyMed acquisition parameters.

Joint InSAR DEM and deformation estimation in a Bayesian framework

BASELICE, FABIO;FERRAIOLI, GIAMPAOLO;PASCAZIO, Vito;SCHIRINZI, Gilda
2014

Abstract

Within this manuscript a novel technique for joint Digital Elevation Model (DEM) reconstruction and deformation estimation is presented. In particular, a Maximum A Posteriori (MAP) estimator that makes use of Gaussian Markov Random Fields (MRF) is proposed. The advantage of the approach, with respect to classical Permanent Scatterers (PS) based techniques, consists of its ability to evaluate the height and the deformation for all resolution cell across the scene, instead of only strong scatterers. Thus, the method is able to work also in natural scenarios, or in general when few PS are available. First results are presented on a simulated dataset with COSMO-SkyMed acquisition parameters.
978-1-4799-5775-0
978-1-4799-5775-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/32380
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