Polyhedral corner reflectors, comprising multiple identical trihedral corner reflectors, represent significant passive interference sources in the radar field. Representative types include the octahedron triangular trihedral corner reflector (OTTCR) and the icosahedron triangular trihedral corner reflector (ITTCR). However, their polarimetric scattering mechanisms are unclear and remain inadequately characterized. This work conducts a comprehensive analysis of high-dimensional scattering characteristics in polyhedral corner reflectors via a multi-domain joint characterization approach. The main contributions are threefold. First, a general co-polarization correlation pattern interpretation tool is developed to explore and utilize target scattering diversity in high-dimensional space. Correlation fluctuation and maximum Gaussian curvature are proposed to quantitatively characterize global statistical and local geometric properties of the general co-polarization correlation pattern, respectively. Second, quantitative investigations are conducted to disclose the variation law of polyhedral corner reflectors’ high-dimensional scattering mechanisms in terms of different radar bands, corner reflector sizes, and sea states with electromagnetic computation data. Finally, the anechoic chamber and field experiments on polyhedral corner reflectors in different bands are conducted. Experimental results demonstrate the effectiveness of the proposed polyhedral corner reflectors multi-domain joint characterization approach with competitive performance in qualitative and quantitative aspects.

Polarimetric synthetic aperture radar (PolSAR) plays an important role in remote sensing. As a valuable application, PolSAR ship detection receives great attentions and obtains fruitful achievements recently. Since ship targets’ scattering responses are highly sensitive to the radar grazing angles, robust PolSAR ship detection still faces challenges especially the very low target to clutter ratio (TCR) phenomenon at large grazing angles. How to find stable polarimetric features for robust ship detection at different grazing angles becomes the key scientific problem. This work dedicates to this issue and the main idea is to explore the potentials of polarimetric roll-invariant features which are relatively independent to radar looking directions. The main contributions are three-fold. Firstly, quantitative investigations are conducted to disclose the variation law of ship targets and sea clutters’ polarimetric scattering mechanisms in terms of different grazing angles with electromagnetic computation data and PolSAR datasets. Secondly, the performances of polarimetric roll-invariant features are significantly examined with the TCR and the absolute difference (AD) indexes. Five optimal polarimetric roll-invariant features with stable TCRs over the wide grazing angle range are founded, which can clearly enhance the contrast between ship targets and sea clutters at large grazing angles. Finally, robust PolSAR ship detection approaches are established with the selected polarimetric roll-invariant features. Comparison studies with Gaofen-3 and Radarsat-2 PolSAR datasets of different grazing angles are carried out. Compared with traditional polarimetric features, the experimental results demonstrate that the selected polarimetric roll-invariant features exhibit superior detection performances in terms of both detection accuracy and detection robustness.

Polarimetric radar enables the acquisition of fully polarimetric information from targets. The electromagnetic scattering response of radar targets exhibits strong dependencies on the polarization basis, time, radar frequency, and incident angle. Most existing methods for characterizing target scattering mechanisms are limited to single-domain features or independent multi-domain features, thereby hindering the achievement of fine target understanding and investigation. This work is dedicated to addressing this issue, with the core idea of transforming from the traditional paradigm of independent single-domain representation to a joint-domain processing framework. First, a polarimetric scattering tensor characterization approach is constructed to integrate target scattering information across multiple domains, including the polarimetric, time, frequency, and spatial domains. Then, a visualization and characterization tool named general polarimetric correlation pattern (GPCP) is proposed for mining and utilizing target joint-domain scattering characteristics. This tool enables the visualization of scattering diversity variations across different dimensions, which encompass rich information rarely considered in existing studies. Investigations are conducted to reveal canonical structures’ latent scattering patterns using electromagnetic computation data. On this basis, a set of polarimetric features, including 8 geometric features and 8 statistical features, are derived to quantitatively characterize these patterns. Quantitative investigations based on the target-to-clutter ratio (TCR) index with Radarsat-2 and Gaofen-3 polarimetric synthetic aperture radar (PolSAR) datasets demonstrate the superiority of the proposed polarimetric features in enhancing ship target contrast. Finally, potential future applications of the proposed GPCP are discussed.

Polarimetric radar, which can acquire full polarization information, has become mainstream in microwave remote sensing. However, radar target scattering responses are strongly orientation dependent, which makes applications such as target detection and recognition more difficult. Polarimetric roll-invariant features, which are independent of target orientations along the radar line of sight (LOS), exhibit great importance and have achieved successful applications in many fields. During the development of radar polarimetry, a number of polarimetric roll-invariant features have been found and reported. A comprehensive summary and investigation of these polarimetric roll-invariant features is necessary and valuable for current studies and further developments. This article is dedicated to this purpose and contains two main ­contributions. First, according to the authors’ knowledge, polarimetric roll-invariant features are thoroughly investigated. Based on their original derivation fashion, they are partitioned into six groups, including those derived from the Sinclair matrix, the Graves matrix, the polarimetric coherency and covariance matrices, eigenvalue–eigenvector-based decompositions, polarimetric coherence/­correlation patterns, and the Kennaugh matrix. Meanwhile, their expressions, inner relationships, and physical interpretations are further examined and presented. Second, since ship detection is an important application for polarimetric synthetic aperture radar (PolSAR), the potential of these independent features for PolSAR ship detection is investigated and demonstrated. Experimental studies with Radar Satellite-2 ( RADARSAT-2 ) and Gaofen-3 PolSAR datasets are carried out. It is validated that the selected polarimetric roll-invariant features exhibit superior performance compared with normal ­polarimetric features.

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

Man-made target recognition is of great significance for many applications within microwave remote sensing. The scattering diversity of various man-made target structures makes radar target identification a difficult task. This work aims at mitigating this issue by mining and utilization of man-made target scattering diversity in polarimetric rotation domain with the interpretation tool of polarimetric correlation pattern. The optimal polarimetric roll-invariant feature set is collected from polarimetric correlation pattern. Then, a polarimetric roll-invariant feature coding scheme is developed for man-made target structure recognition. Moreover, polarimetric radar measurement errors in terms of channel coupling and imbalance are also considered. Experimental studies with electromagnetic computation datasets including canonical structures and an unmanned aerial vehicle (UAV) target and real spaceborne polarimetric synthetic aperture radar (PolSAR) data of a ship target are carried out. Compared with the Cameron decomposition, the proposed method exhibits better recognition performance and stronger robustness, especially for oriented man-made structures.