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17 May 2006Feature 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.
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Julie Ann Jackson, Randolph L. Moses, "Feature extraction algorithm for 3D scene modeling and visualization using monostatic SAR," Proc. SPIE 6237, Algorithms for Synthetic Aperture Radar Imagery XIII, 623708 (17 May 2006); https://doi.org/10.1117/12.666558