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Synthetic Aperture Radar Image Formation with Uncertainty Quantification

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Document pages: 6 pages

Abstract: Synthetic aperture radar (SAR) is a day or night any-weather imaging modalitythat is an important tool in remote sensing. Most existing SAR image formationmethods result in a maximum a posteriori image which approximates thereflectivity of an unknown ground scene. This single image provides noquantification of the certainty with which the features in the estimate shouldbe trusted. In addition, finding the mode is generally not the best way tointerrogate a posterior. This paper addresses these issues by introducing asampling framework to SAR image formation. A hierarchical Bayesian model isconstructed using conjugate priors that directly incorporate coherent imagingand the problematic speckle phenomenon which is known to degrade image quality.Samples of the resulting posterior as well as parameters governing speckle andnoise are obtained using a Gibbs sampler. These samples may then be used tocompute estimates, and also to derive other statistics like variance which aidin uncertainty quantification. The latter information is particularly importantin SAR, where ground truth images even for synthetically-created examples aretypically unknown. An example result using real-world data shows that thesampling-based approach introduced here to SAR image formation providesparameter-free estimates with improved contrast and significantly reducedspeckle, as well as unprecedented uncertainty quantification information.

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