Stochastic modelling for structure reconstruction from high-resolution SAR data

The exploitation of metric resolution SAR data for the reconstruction of the structure of the observed scenes poses specific problems related both to the complexity of acquired scene details and to the peculiarities of the SAR acquisition system. On the one hand, much more complexity is transferred through the system from the scene into the data: new kinds of complex man-made scene objects are acquired. Layover and shadowing and responses from single scatterers tend to dominate the data. On the other hand, multiple signal reflections, sidelobe effects, radiometric pollution and many other effects related to the increased resolution of the system have to be taken into account. We show how, by properly modelling in stochastic terms the peculiarities of both the acquisition system and of the scene and by composing them in a Bayesian framework, new methods are developed that allow the reconstruction of the imaged structures from SAR data. Particular interest is devoted to the application of the developed algorithms in urban environments on data resolutions ranging from a few metres to fifty centimetres.