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The side-looking imaging geometry of synthetic aperture radar (SAR) causes inevitable layover in SAR images. Separating the contributions from different scatterers has been the fundamental for many applications. It is typically solved by explicit inversion of the SAR imaging model to retrieve the scattering profile along the mixed dimension (elevation), which is otherwise known as SAR tomography....
Multibaseline SAR interferometry may face unmodeled interferometric phase such as unmodeled motion phase and uncompensated atmospheric phase, as well as non-Gaussian statistics in the context of distributed scatterer. We developed the robust InSAR optimization (RIO) [1] framework to systematically tackle these issues. Experiments show that RIO outperforms the current multibaseline InSAR methods in...
This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption...
This paper introduces the Robust InSAR Optimization (RIO) framework to the multi-pass InSAR techniques, such as PSI, SqueeSAR and TomoSAR whose current optimal estimators were derived based on the assumption of Gaussian distributed stationary data, with seldom attention towards their robustness. The RIO framework effectively tackles two common problems in the multi-pass InSAR techniques: 1. treatment...
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