|Stochastic Systems Group|
In this talk, I will present a method that we have developed for the formation of complex-valued spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Prior information is included through non-quadratic potential functions, and we demonstrate the use of a variety of such functions in this framework. The technique effectively deals with the complex-valued, random-phase nature of the underlying SAR reflectivities. Efficient and robust numerical solution of the optimization problem is achieved through extensions of half-quadratic regularization methods to the complex-valued SAR problem.
Important attributes for automated decision making from SAR images include the resolution of the formed image, the presence and degree of artifacts such as sidelobes and speckle in the image, and the clarity of the shapes and boundaries of the objects present in the scene. Compared to conventional image formation schemes, our approach offers increased resolvability of point-scatterers, ease of region segmentation, reduced sidelobes and reduced speckle. These improvements are demonstrated through a quantitative, feature-based evaluation on a large data set of real SAR images, as well as on synthetic examples.
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