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

Abstract

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.

Polarimetric synthetic-aperture inversion for extended targets in clutter

“…Note we have used the triple product, or BAC-CAB, identity as in (12) and the tensor notation as in (13). The assumption (20) converts (18) to the form…”

“…SCR versus MSE for the standard reconstructed images and coupled reconstructed images, respectively, vertically polarized target. is visible in the data over a range of aspects; in other words, assumption (20) was not used in producing the simulated data. We plot the SCR versus the MSE in figure 6.…”

Canonical Scattering Feature Models for 3D and Bistatic SAR

“…The models are derived from planar GTD responses and from products of these 2D responses. The models provide a significant generalization of 2D, monostatic scattering models [3][4][5][6][7][8] to both bistatic (or multistatic) measurements and to nonplanar radar measurement apertures that provide 3D scene information. In addition, the fully-polarimetric amplitudes of these responses are modeled from the SAR geometry and the orientation of the shapes.…”

“…These parametric models characterize the dominant radar backscattering returns of an object, and provide for compact, geometrically-relevant descriptions of the dominant energy components of a backscattered signal or of the resulting formed SAR image. The authors of [3]- [8] propose attributed scattering center models for monostatic returns from several canonical reflectors, including trihedrals, dihedrals, and top-hats. These models have given promising results when applied in automatic target recognition (ATR) algorithms [3,[6][7][8][9].…”

“…The authors of [3]- [8] propose attributed scattering center models for monostatic returns from several canonical reflectors, including trihedrals, dihedrals, and top-hats. These models have given promising results when applied in automatic target recognition (ATR) algorithms [3,[6][7][8][9]. The model parameters form useful feature sets for feature-based detection or classification [3] and tracking [10], for target fingerprinting, and for ATR performance prediction [9].…”