As polarimetric antennas can be isolated through excellent electronic frameworks in circular quad-polarization (CQP) synthetic aperture radar (SAR) systems, cross-polarization (x-pol) and co-polarization (co-pol) channel imbalances are more challenging and essential to calibrate than crosstalk in polarimetric calibration (PolCAL). In uncalibrated CQP SAR images without corner reflectors (CRs), the reciprocity and reflection symmetry assumptions of the distributed targets are commonly used to estimate the x-pol and co-pol channel imbalances, respectively. To suppress the influence of additive noise on determining channel imbalances through distributed targets, high signal-to-noise ratio (SNR) distributed targets should be obtained from the x-pol and co-pol channels of the CQP SAR images: namely, surface-dominated and volume-dominated targets. However, some reflection symmetry assumptions used in the existing calibration literature have poor applicability with volume-dominated targets, resulting in unsatisfactory estimation results for the co-pol channel imbalance phase. In this paper, we assess the priority of the reflection symmetry properties of volume-dominated targets used to calibrate the co-pol channel imbalance phase in CQP SAR data synthesized from linear quad-polarization data of ALOS, GF-3, and RADARSAT-2. In the theoretical part, high-priority reflection symmetry (termed semireflection symmetry) assumptions are confirmed as the most suitable for estimating the co-pol channel imbalance phase, and were selected to develop an algorithm for estimating the co-pol channel imbalance phase. Furthermore, based on the novel method for estimating the co-pol channel imbalance phase, a channel imbalance calibration scheme is proposed for CQP SAR systems with reciprocal crosstalk, including extracting surface-dominated and volume-dominated targets, and estimating and filtering channel imbalances. We demonstrate the effectiveness of our proposed scheme with CRs in simulated CQP SAR images. The experimental results show that the calibration scheme is an effective workflow for estimating channel imbalances in CQP SAR systems with reciprocal crosstalk.

Calibration by distributed targets is an important part of polarimetric calibration (PolCAL) without corner reflectors (CRs). Currently, both the correlation of HH and VV ( $R_{\rm{hhvv}}$ ) and the equivalent number of looks (ENL) constitute the common method for the extraction of Bragg-like points (BLPs) in order to calibrate the co-polarization channel imbalance $k$ . However, strict assumptions about distributed targets and complex mathematical expressions regarding ENL limit the impact analysis of $k$ on $R_{\rm{hhvv}}$ and ENL; thus, the fixed thresholds for $R_{\mathrm{hhvv}}$ and ENL can be obtained only by comparing the calibration errors with the CRs or simulated values in order to calibrate $k$ accurately. In some Earth observation and lunar exploration without CRs, the above conditions are not satisfied. In this article, a BLP extraction method is proposed for calibrating co-polarization channel imbalance $k$ in PolCAL. ${H}/{\bar{\alpha }}$ decomposition is utilised to deduce the specific scattering impacts of BLPs by different $k$ values first. Then, a dynamic selection method is proposed to reduce the influence of fixed thresholds and improve the extraction accuracy. Multi-scene polarimetric synthetic aperture radar (PolSAR) images from AIRSAR, ALOS, and GF-3 are utilised to verify the effectiveness of the proposed algorithm in the extraction of BLPs and the calibration results of $k$ obtained by applying the extracted BLPs (ext. BLPs).

This paper proposes a generalized compact polarimetric (GCP) mode along with two optimal polarization mode selection parameters to address the challenges of polarization mode selection in classification tasks across diverse scenarios. Theoretically, we conduct an in-depth analysis of the differences between circular and linear transmit polarizations, demonstrating their fundamental equivalence in terms of information content. For the first time, we propose that different classification tasks require different optimal polarization modes, and the optimal transmit polarization mode may lie in the elliptic polarization domain of SAR systems rather than traditional circular compact polarimetric (CP) or linear dual-polarimetric (DP) modes. The proposed approach is validated using full-polarimetric SAR data from San Francisco and Hainan, showing that the optimal elliptical polarization mode achieves classification accuracies that are 2% to 42% higher than those of traditional CP or DP modes for certain categories, and performs comparably to full polarization. This improvement in accuracy stems from the interaction between the transmit polarization and the target scene, rather than advancements in classification algorithms. Using the two proposed parameters, the overall and category-specific classification performance of GCP modes can be effectively evaluated, enabling the identification of the optimal polarization mode for a given task. These findings provide significant insights for the design of future polarimetric SAR systems and offer new perspectives and directions for mission planning and mode selection for on-orbit satellites.

The calibration of channel imbalances is currently the main concern in polarimetric calibration (POLCAL) since the crosstalk of recent polarimetric synthetic aperture radar (Pol-SAR) systems is lower than −20 dB. The existing channel imbalance calibration method without corner reflectors utilizes both volume-dominated and Bragg-like targets. However, there are two limitations to using volume-dominated targets. One is that the inaccurate selection of volume-dominated areas in the uncalibrated Pol-SAR images has a negative influence on the estimation of cross-polarization (x-pol) channel imbalance, which subsequently impacts the estimation of copolarization (copol) channel imbalance. The other is that there are minimal volume-dominated areas in some special applications of Pol-SAR, such as planetary exploration. Thus, only selecting Bragg-like targets to estimate the values of both transmitting and receiving channel imbalances, which is proposed in this paper, can avoid the uncertainty brought about by selecting other distributed targets in an uncalibrated imaginary. In addition, the reciprocity assumption and characteristics corresponding to H/$\overline{\alpha }$ decomposition are introduced to eliminate the phase ambiguity for the first time. Compared with previous methods, our method had an obvious advantage in terms of universality, since Bragg-like targets are common in the most illuminating areas. The novel method was applied to both the simulated data from the L-band Advanced Land Observing Satellite (ALOS) and C-band GaoFen-3 (GF-3), and to real data with corner reflectors on site. The results from the simulated data showed that the errors of the amplitude and phase estimation were less than 0.5 dB and 5.0° in most topographical features. Meanwhile, the $VV$/$HH$ terms from all trihedral corner reflectors were less than 0.3 dB for amplitude, and 5.5° for phase after calibration by using the estimated channel imbalances.

Manual selection of the distribution target and determination of its spatial location is not only time consuming but also has a serious impact on the polarimetric channel imbalances assessment if the area is incorrect. However, the existing automatic extraction methods are affected by the morphology of the features, and their local feature extraction capability is limited. On the other hand, only local information is considered, while the feature channels and global information are ignored, which in turn leads to the limited application scenarios, especially in some urban areas. The polarimetric channel imbalance estimation accuracy is so low that it exceeds the system tolerance limit. Therefore, in order to more effectively mine the polarimetric features of distributed targets in PolSAR images, we propose an attention-based multipath hybrid convolutional distribution target extraction network(AMHC-DTENet) for polarimetric channel imbalance assessment. Firstly, in order to develop the local feature extraction capability of the hierarchical network, we design a hybrid convolutional module with adaptive adjustment of the receptive field size. Secondly, a polarimetric feature channel reconstruction module is constructed in order to utilize the spatial information of the polarimetric feature channels. Then, considering that the polarimetric information of the ground feature is sensitive to the relative geometry of the target attitude to the radar line of sight, the Vision Transformer architecture is used to capture polarimetric global information and extend it to multipath. Finally, Gaofen-3(GF-3) full-polarimetric data is used for experimental verification. Experimental results demonstrate the effectiveness and reliability of the proposed method.

Polarimetric calibration is crucial to ensure the accuracy and reliability of subsequent application results. Since Gaofen-3(GF-3) has been providing polarimetric Synthetic Aperture Radar (Pol-SAR) images for 6 years, it is necessary to monitor the polarimetric performance by estimating the residual channel imbalance. In this paper, we proposed an improved algorithm based on zero helix component of Bragg- like targets. The method can provide transmitting and receiving channel imbalances results and can be applied to the large-scale polarimetric evaluation. We verified the effectiveness of the algorithm and assessed the residual channel imbalance in the multi-beam polarimetric products of GF-3 quad-polarimetric Stripmap (QPSI) from 2019 to 2023. The experiments indicates that the residual channel imbalance changes in the same strip observation and the polarimetric performance remains stable. The evaluation conclusions can be used as references for subsequent application of GF-3 polarimetric products.