Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples.

Polarimetric calibration (PolCal) is essential for the quantitative processing of polarimetric synthetic aperture radar data. Traditional distributed target methods typically require at least one corner reflector (CR) to determine the copolarization (co-pol) channel imbalance. However, the deployment of CRs is costly and impractical in challenging areas, and therefore, advanced PolCal methods that do not rely on CRs have been developed. One widely used method estimates crosstalk and cross-polarization channel imbalance based on volume-dominated pixels. This method estimates co-pol channel imbalance based on Bragg-like pixels, using the Gauss−Newton method according to the unitary zero helix (UZH) constraint and obtains the final co-pol channel imbalance through fitting. However, the selection of initial values and step sizes affects the convergence of the Gauss−Newton method, while the fixed threshold used in fitting impacts the accuracy of sample selection. These issues collectively influence the precision of the calibration results. Therefore, we proposed an iterative adaptive PolCal method to improve the UZH method from a computational perspective. This method first uses an iterative process to obtain a more accurate global initial value for each block's initial input. It then combined the Levenberg−Marquardt algorithm to adjust step sizes and solve for each block. In addition, the variation coefficient was introduced to achieve adaptive sample selection and enhance fitting accuracy. The effectiveness of our proposed method was validated using GF3 02 satellite two-scene calibration field images. The experiments demonstrated that the proposed method not only ensured convergence but also improved the accuracy and stability of PolCal methods without CRs.

Polarimetric synthetic aperture radar (PolSAR) calibration quality assessment is essential for verifying the reliability of polarimetric calibration results and ensuring the accuracy of subsequent quantitative applications. The corner-reflector-based assessment is accurate but depends on field deployment and maintenance, whereas the distributed-target-based methods is easier to automate but is sensitive to mixed scattering within image patches and to non-unique histogram peaks. In addition, the parameter distribution may contain multiple peaks, affecting the uniqueness and stability of assessment. To address these problems, this paper proposes a robust distributed-target-based PolSAR calibration quality assessment (RD-PCQA) method without corner reflectors (CRs) to address these problems. The proposed method first uses hypothesis testing of confidence interval method for PolSAR calibration (PCHTCI) to extract high-quality distributed targets and combines the polarimetric correlation coefficient R_HHVV to select volume-scattering-dominant targets, thereby improving the physical consistency of samples used for channel imbalance amplitude (CIA) estimation. Second, a high-proportion distributed-target constraint is used to refine the samples for channel imbalance phase (CIP) and polarimetric crosstalk estimation, reducing the influence of nonideal scatterers on parameter estimation. Finally, a unique peak searching strategy based on progressively enlarged statistical scales is proposed to suppress the effect of multi-peak distributions on assessment result. Experiments were conducted using three GF-3 PolSAR images acquired over the SAR calibration site in Etuoke Banner, Ordos, Inner Mongolia, China, with CR results used as references, and considering finite sample uncertainty. The experimental results show that, compared with the conventional distributed-target-based method, the proposed method is closer to the corresponding CR mean in all comparisons, with the mean absolute deviations for CIA, CIP and crosstalk scenarios reduced to 0.024 dB, 2.043° and 3.069 dB, respectively. Therefore, it demonstrates the effectiveness and practical potential of the proposed method for PolSAR calibration quality assessment without CRs.