An assessment of polarimetric inverse synthetic aperture radar (Pol-ISAR) imaging is accomplished for realistic target models with the use of our recently developed high-frequency radar cross-section simulator tool called PREDICS. X-band, narrow-angle and full-polarimetric ISAR data for the CAD models of the well-known test object SLICY and a ground vehicle, namely a backhoe loader are analyzed to infer their structural characteristics. Experimental data obtained from a tower-turntable ISAR measurements of a T-72 tank target have also been utilized to assess the validity of the simulator. First, the intensity images in linear and circular polarization bases are directly utilized to evaluate the data quality and to characterize target features for classification. Then, the Pauli image decomposition scheme is applied to separate the basic scattering mechanisms occurring at target pixels. The identifiable canonical target forms are successfully extracted as single, double and multi-bounce scattering mechanisms that are pinpointed at their true locations. The results indicate that PREDICS is able to generate high-fidelity synthetic Pol-ISAR signatures of complex targets whereby successful interpretation of innumerous target scattering mechanisms and features can be achieved through Pauli decomposition scheme for classification purposes.

Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the $H$ / $\overline{\alpha }$ plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth.

Ground-based synthetic aperture radar (GB-SAR) systems are mostly utilized to be practical practices in improved understanding of the complex mechanism of microwave backscattering. They also provide complementary information on evaluating the validity of the polarimetric analysis of air-borne or satellite-borne SAR applications. This study investigates some capabilities of polarimetric L-band GB-SAR imaging by testing its performance against a typical terrain and various kinds of manmade targets. Trihedral corner reflectors are also included in the analyses because of their importance in data calibration. Polarimetric backscattering signatures of different targets are analyzed in terms of qualitative assessment of amplitude images and identification and classification of scattering mechanisms through target decomposition techniques. The findings of these analyses and detailed discussions are presented. Specifically, the entropy/mean-alpha ((H/α ̅)) classification results are shown to be capable of clearly identifying the dominant scattering mechanisms occurring within the investigated scene.

In this study, a filtering method based on the threshold value of normalized synthetic aperture radar (SAR) data is proposed to eliminate clutter in millimeter wave ground based synthetic aperture radar (GB-SAR) images. In the proposed method, first, stepped frequency continuous wave SAR data are reconstructed by using the back-projection algorithm and focused complex SAR data are obtained. Then, the amplitude values of the complex SAR data are normalized and the best threshold values to distinguish the target from clutter is determined by the OTSU's thresholding method. Next, a filter mask is created that cancels all data below the computed threshold values. The mask matrix is finally multiplied with the resulted GB-SAR data to eliminate all clutter from the image. With the proposed technique, the best threshold value is determined automatically by directly processing the raw data without converting the SAR data into any RGB images. The proposed technique is validated through real GB-SAR experiments that were carried out in the frequency band of 78-81 GHz. In the experiments, challenging GB-SAR data are obtained using high cluttered background materials, and very successful filtering operations are performed with the proposed technique.