We present results of large-scale experimentation with the enhanced model-based despeckling (EMBD) filter aiming at its validation from a pragmatical point of view. Furthermore, we point out criteria for the choice of a prior model for synthetic aperture radar (SAR) images. These criteria rely on an evidence maximization step -part of the EMBD algorithm itself- and on the verification of the obtained speckle statistics against the assumptions made in the filtering.
The reconstruction of urban structures from InSAR (Interferometric Synthetic Aperture Radar) observations is a complex task. Until now it has been tipically approached using the methods of radargrammetry and SAR interferometry, in a direct extension of what had been done in the past for the reconstruction of natural surfaces from generally much lower resolution data. In this work, we present a new concept aiming at the accurate and detailed reconstruction of the observed city scenes for metric SAR observations. We use a model-based approach for the synergetic analysis of the different sources of information in InSAR data. We define a hierarchical model of the InSAR observation that is both deterministic and stochastic. While the deterministic section describes the SAR imaging geometry and its effects and expresses the different scene structures, the stochastic part incapsulates instead prior knowledge about the signal and defines its attributes while also describing incertitude over the parameters of the geometrical model. Bayesian inference is used to couple the diffent levels of the model, and to further define parameter estimation algorithms.
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