This article proposes a generalized compact polarimetry (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 synthetic aperture radar (SAR) systems rather than traditional circular compact polarimetric (CP) or linear dual-polarization (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 insight into the design of future polarimetric SAR systems and offer new perspectives and directions for mission planning and mode selection for on-orbit satellites.

The accuracy of the polarimetric evaluation of low frequency polarimetric synthetic aperture radar (PolSAR) systems based on distributed targets is uncertain when the ionospheric information is unknown. Therefore, in this paper we investigate the potential of polarimetric evaluation of LT-1 images based on a set of calibrators arranged for L-band LT-1 PolSAR images. First, reasonable assumptions are made for common calibration models for LT-1 images. The ideal backscatter matrix of the set of calibrators is subsequently used in conjunction with the polarimetric distortion matrices (PDMs) to construct constraints for solving the PDMs. Finally, for possible ambiguous values of the solution, we use phase consistency as well as the Frobenius norm to remove the ambiguous values of the PDMs and obtain the final evaluated results. In the experimental part, we verify the feasibility of the method based on simulation data. Thus, the method contributes to accurate evaluation of LT-1 images.

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) has been well-established for forest height estimation. However, employing mono-frequency SAR data for PolInSAR tree height inversion presents inherent limitations, posing challenges to ensure inversion accuracy. This article presents a novel method for the inversion of vegetation parameters using dual-frequency (DF) four-stage PolInSAR, aiming to address the limitations observed in mono-frequency inversion. By leveraging the differential penetration of vegetation across distinct frequency bands, this method facilitates the derivation of more precise volume-only coherence and ground phase information. Applying the DF four-stage PolInSAR method to a substantial dataset of simulation results identifies the optimal band combination as P- and L-band. Moreover, the band combination that yields the most significant enhancement in accuracy is determined to be L- and S-band. These simulation results inform the design of the DF full-polarization SAR system. Subsequently, airborne SAR data are acquired using this L- and S-band full-polarization airborne SAR system over the Saihanba Forest Farm in Hebei, China. Ground-based LiDAR measurements serve as reference values for the comparison of PolInSAR inversion results. The DF four-stage PolInSAR method has a 5.46% improvement in the inversion accuracy of airborne SAR data. Both simulation and airborne SAR data inversion outcomes demonstrate a significant enhancement in forest height inversion accuracy achieved through the DF four-stage PolInSAR method compared to the mono-frequency approach.

The dual-frequency (DF) synthetic aperture radar (SAR) in lunar orbit, using L and S bands, is the only full-polarization (FP) SAR. It explores different layers of the moon, relying on the unusual rotational symmetry for polarimetric calibration. Addressing prior issues in polarimetric evaluation, this study tackles the challenges of an expanding dataset (now exceeding 900 scenes) and the necessity to evaluate polarimetric distortion (PD) values in the range direction. A novel polarimetric evaluation framework is introduced, enhancing existing methods. First, a scheme is proposed to eliminate low-quality areas in the range direction based on antenna isolation specifications, validated theoretically with simulated and real data. Second, more than 900 DFSAR scenes, downloaded prior to April 25, 2023, are utilized for evaluation, surpassing previous limitations and enhancing accessibility for users. By evaluating the L-band data, the image calibration results acquired by DFSAR between September 19, 2019, and December 4, 2020, have high accuracy and can be prioritized for lunar applications. In addition, we recommend that researchers pay attention to the issue of changing data quality for releases around March 2021.The amount of data in the S-band with the L-S-joint mode is small, and we provide the availability results directly. Third, the study applies the evaluated results to lunar polarization research, contributing to lunar exploration through polarimetric parameters. This comprehensive framework ensures accurate data utilization, addressing the evolving needs of lunar exploration with improved methodologies and expanded datasets.

The channel imbalance and crosstalks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) synthetic aperture radar (SAR) systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, and so on. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and crosstalks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross- and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the crosstalks. Then, the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

For synthetic aperture radar (SAR), multipolarization and multifrequency modes greatly enrich the acquired earth resource information and have been widely applied in remote-sensing fields. In this article, we compare the classification capabilities of multipolarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual-polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For polarimetric SAR (PolSAR) system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multipolarization and dual-frequency SAR payloads and the design of future SAR systems.

In this paper, we investigate the potential of polarimetric synthetic aperture radar (SAR) in identifying flat areas using fractal dimension and polarimetric scattering similarity. A two-step method is proposed, including rough selection and fine selection. First, rough selection is performed by calculating the fractal dimension of the radar backscattered total power image. Then for each candidate region, the fine selection is conducted using polarimetric scattering similarity parameters. Furthermore, the effectiveness of the method is verified by GF-3 quad-polarimetric SAR data and SRTM1 DEM data in desert areas of China. Results show that for the final selected flat area (320 × 320 m), the maximum elevation deviation is 3.39 m and the elevation standard deviation is 0.72 m. Therefore, without depending on additional DEM data, the proposed method can effectively achieve flat areas identification, which can be helpful for the future application of polarimetric SAR data in the Moon.

Due to the large equivalent transmit crosstalk in hybrid quadrature polarimetric SAR system, traditional calibration methods using azimuthally symmetric distributed targets (AS-Targets) cannot work. In this manuscript, a preprocessing method of AS-Targets' covariance matrix is proposed, which makes equivalent crosstalk after processed become small to achieve system calibration through the AS-Targets. Simulation and airborne SAR data testing verify the reliability of the proposed method. The results show that the method presented in this manuscript can well calibrate the hybrid quadrature polarimetric SAR system using AS-Targets.

In previous publications, it is generally believed that the rotated buildings in built-up areas can induce polarization orientation angles, which cause overestimation of the volume scattering contribution. However, we found that the rotation effect of the double-bounce scattering wall-ground structures cannot be accurately expressed by an orientation angle. In this letter, dihedrals are used to simulate the wall-ground structures, and a novel technique for inversion of rotation angles in built-up areas is proposed. This letter is committed to directly inverting rotation angles rather than orientation angles. Then the rotation angles are compensated into the measured scattering matrix instead of deorienting the coherence matrix. C-band GF-3 and L-band ALOS-2 polarimetric data of San Francisco are used to verify the effectiveness of the proposed inversion technique.

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%–4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%–3% compared with QP. These conclusions can provide guidance for future payloads’ design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems.

Hybrid-polarimetric architecture may become a new option for spaceborne quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) mainly due to the range ambiguity improvement without loss of polarization information. To inherit the analytical methods developed for the conventional quad-pol SAR data, it is necessary to transform the hybrid quad-pol SAR data into a linear polarimetric basis. However, this operation will result in deterioration of azimuth ambiguity performance. The theoretical analysis on range and azimuth ambiguities in quad-pol SAR has been relatively complete, which is also summarized in this article. Yet, there is no real data to verify the equivalence of polarization information and to compare the ambiguity levels. Based on P-band airborne experiment, real hybrid and conventional quad-pol SAR data are used for comparative verification. In addition, considering that polarimetric target decomposition is often used in the practical applications, we suggest a new perspective from target decomposition for the ambiguities in quad-pol SAR. Identical polarimetric processing results for two sets of comparative data verify that hybrid quad-pol SAR data are indistinguishable from conventional quad-pol SAR data. The demonstrated intensity images and decomposed RGB images with azimuth ambiguities, acquired by the two quad-pol modes, completely conform to the theory.

This paper established a unified framework to compare the capabilities of different polarimetric SAR systems in target detection based on polarimetric covariance matrix. The framework contains two different strategies. The first is based on the original polarimetric matrix and the second is based on the main scattering matrix. An improved algorithm is proposed based on the second strategy. GF-3 quad-polarimetric data is used to compare the performance between different algorithms, the results show that the proposed algorithm can obtain the highest signal-to-clutter ratio (SCR). The performance of different polarimetric systems is further compared, and the results show that the quad-polarimetric (QP) performs best, compact-polarimetric (CP) performance is similar to QP, and dual-polarimetric (DP) performs worst.

In this letter, a four-component decomposition method of polarimetric synthetic aperture radar interferometry (PolInSAR) using refined volume scattering models is proposed. In the proposed algorithm, the volume scattering models under the assumption of reflection symmetry are refined by employing the orientation angles. In addition, the polarimetric interferometric similarity parameter (PISP) parameter is introduced to modify the refined volume scattering models. Airborne L-band PolInSAR data are used to evaluate the performance of the method. The experimental results demonstrate that the proposed method can effectively overcome the overestimation of the volume scattering and characterize the scattering mechanisms of various terrain types.

In this letter, a hierarchical extended multicomponent decomposition method (ExMCSM) of polarimetric synthetic aperture radar interferometry (PolInSAR) is proposed. The target of this method is to overcome the overestimation of volume scattering (OVS) and to mitigate the mixed ambiguity of the scattering mechanism. In the proposed method, prior to the decomposition, orientation angle compensation (OAC), and helix angle compensation (HAC) are applied to the coherency matrix to reduce the complexity of the operation. The polarimetric interferometric similarity parameter (PISP) is used to refine the volume scattering models, and the optimal coherence magnitude (γopt3) is used as a criterion for the adaptive selection of these volume scattering models. The efficiency of the proposed method is demonstrated using two different test sites. Experimental results demonstrate that the proposed method can effectively represent the scattering characteristics of the ambiguous regions.

The unsteadiness of the low altitude platforms leads to uneven light and shade in the azimuth of synthetic aperture radar (SAR) images. The traditional method for the azimuth intensity adjusting of SAR images has two important disadvantages. The first is its discontinuity of the normalization factor, and the second is its inapplicability on regions with broken terra of very weak or very strong back scatterer. This paper is focused on cutting down the discontinuity of the normalization factor and reducing the affection of the broken terra on the normalization factor. A new method is proposed in the adjustment of the intensity variance in the azimuth of SAR images by the image data in combination with the cross roll angle of platforms. The experiments and results show its good availability and robustness of the new method.

The Polarimetric Whitening Filter (PWF) method for speckle reduction in polarimetric synthetic aperture radar (SAR) is stated with some traditional covariance matrix parameter estimate methods. Aimed at their defects in parameter estimation, an enhanced auto-adaptive edge-detection method for parameter estimating is presented. Because of the lack of point target and line detection in the above-mentioned methods, a new parameter estimation method is then introduced. Finally, results of the above-mentioned methods applied in PWF are submitted. Detail analyses and comparisons are also given. It is proved that the new parameter estimation method is the most robust.