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Feature extraction algorithm for 3D scene modeling and visualization using monostatic SAR

We present a feature extraction algorithm to detect scattering centers in three dimensions using monostatic synthetic aperture radar imagery. We develop attributed scattering center models that describe the radar response of canonical shapes. We employ these models to characterize a complex target geometry as a superposition of simpler, low-dimensional structures. Such a characterization provides a means for target visualization. Fitting an attributed scattering model to sensed radar data is comprised of two problems: detection and estimation. The detection problem is to find canonical targets in clutter. The estimation problem then fits the detected canonical shape model with parameters, such as size and orientation, that correspond to the measured target response. We present an algorithm to detect canonical scattering structures amidst clutter and to estimate the corresponding model parameters. We employ full-polarimetric imagery to accurately classify canonical shapes. Interformetric processing allows us to estimate scattering center locations in three-dimensions. We apply the algorithm to scattering prediction data of a simple scene comprised of canonical scatterers and to scattering predictions of a backhoe.