This thesis presents a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise, the investigation of the inherent low rank property of object-based InSAR phase stacks and two tensor-decomposition based methods for robustly reconstructing such phase stacks. The quality of the proposed methods are assessed on real data and compared to the state-of-the-art methods.
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This thesis presents a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise, the investigation of the inherent low rank property of object-based InSAR phase stacks and two tensor-decomposition based methods for robustly reconstructing such phase stacks. The quality of the proposed methods are assessed on real data and compared to the state-of-the-art methods...
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