Characterization of polarimetric backscattering phenomenon is investigated through the ground-based synthetic aperture radar (GB-SAR) data that are collected from a test site consisting of manmade targets distributed over a vegetated terrain at L-band frequencies. The characterization of either synthetic or natural targets has been made by applying two main polarization interpretation schemes, namely amplitude-based interpretation and target-based interpretation in extracting the features of those targets. Polarimetric backscattering signatures of objects are analyzed by assessing the Pauli, total power, entropy/mean-alpha (H / α¯ ) images of the terrain for the goal of identification and classification of scattering mechanisms. After applying the classification methodology presented, obtained polarimetric images have demonstrated that target features can be effectively discriminated from each other providing a successful characterization of natural and manmade objects based on GB-SAR measurements. Specifically, H / α¯ classification results are shown to be well capable of clearly identifying the distinct scattering mechanisms of the terrain. Full-polarimetric measurements of this particular scene confirmed the ability to retrieve the physical target features to a certain extent using the high-resolution GB-SAR imagery and the relevant polarimetric analyses.
Two popular synthetic aperture radar (SAR) reconstruction algorithms, namely the back-projection (BP) and the frequency wavenumber (ω−k ) algorithms, were tested and compared against each other, especially for their use in ground-based (GB) SAR applications directed to foreign object debris removal. For this purpose, an experimental setup in a semi-anechoic chamber room was accomplished to obtain near-field SAR images of objects on the ground. Then, the 90 to 95 GHz scattering data were acquired by using a stepped frequency continuous-wave radar operation. The performances of the setup and the imaging algorithms were then assessed by exploiting various metrics including point spread function, signal-to-clutter ratio, integrated side-lobe ratio, and computational complexity. Results demonstrate that although both algorithms produce almost accurate images of targets, the BP algorithm is shown to be superior to the ω−k algorithm due to its some inherent advantages specifically suited for short-range GB-SAR applications.
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