Paper
28 January 2002 Bayesian inversion method for the 3D reconstruction of settlements from metric SAR observations
Author Affiliations +
Proceedings Volume 4543, SAR Image Analysis, Modeling, and Techniques IV; (2002) https://doi.org/10.1117/12.453965
Event: International Symposium on Remote Sensing, 2001, Toulouse, France
Abstract
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.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marco Quartulli and Mihai P. Datcu "Bayesian inversion method for the 3D reconstruction of settlements from metric SAR observations", Proc. SPIE 4543, SAR Image Analysis, Modeling, and Techniques IV, (28 January 2002); https://doi.org/10.1117/12.453965
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Cited by 2 scholarly publications.
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KEYWORDS
Interferometric synthetic aperture radar

Data modeling

Interferometry

Synthetic aperture radar

3D modeling

Feature extraction

Stochastic processes

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