The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase inaccuracy and limiting the unwrapping results of traditional unwrapping methods. With the successful implementation of deep learning in a variety of industries in recent years, new concepts for phase unwrapping have emerged. This research offers a one-step InSAR phase unwrapping method based on an improved pix2pix network model. We achieved our aim by upgrading the pix2pix network generator model and introducing the concept of quality map guidance. Experiments on InSAR phase unwrapping utilizing simulated and real data with different noise intensities were carried out to compare the method with other unwrapping methods. The experimental results demonstrated that the proposed method is superior to other unwrapping methods and has a good robustness to noise.

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 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.

Array Interferometric Synthetic Aperture Radar (Array InSAR) enables high-precision 3D point cloud reconstruction, but data quality is often degraded by environmental noise, channel amplitude–phase errors, and multipath interference. To address the main noise types in urban airborne Array InSAR point clouds—outlier noise, channel amplitude-phase errors, and multipath interference—this paper proposes a multi-scale global denoising algorithm tailored to their characteristics. The proposed method first removes prominent airborne outlier noise using a denoising algorithm based on an adaptive dispersion coefficient. It then adjusts point cloud normal based on cosine similarity and information entropy. To mitigate channel amplitude-phase noise, an adaptive weighted filter incorporating curvature and intensity features is proposed. Finally, multipath interference is mitigated through geometric and backscattering intensity consistency verification. Experimental results demonstrate that MSGDA achieves an ONRI of 0.9184, an EPI above 0.8, a CCI of 0.8134, and a DUI of 0.7307, consistently outperforming comparison algorithms. These results confirm that MSGDA effectively suppresses noise while preserving structural features, thereby enhancing the quality and reliability of Array InSAR point clouds for urban applications.

Array Interferometric Synthetic Aperture Radar (ArrayInSAR) point cloud is a point cloud obtained by three-dimensional imaging of two-dimensional Synthetic Aperture Radar (SAR) images using array interferometric synthetic aperture radar technology, which can eliminate the phenomenon of layover in two-dimensional images and provide new data support for intelligent mapping. However, due to the influence of system thermal noise, baseline error and environment interference, there are noise points in the ArrayInSAR point cloud with uneven distribution and large elevation error, which bring a lot of gross errors to the 3D terrain results. To solve the problem that discrete points in ArrayInSAR point cloud data seriously affect the quality of three-dimensional data, this paper proposes an ArrayInSAR point cloud outlier noise removal method based on hybrid filtering. According to the distribution pattern of noise points, the outlier noise is divided into discrete outlier noise points and clusters of outlier noise. Firstly, the adaptive segmentation of point cloud dispersion coefficient within the k-neighborhood is used to remove discrete outlier noise points, and then the adaptive threshold segmentation algorithm based on the number of in-class points after K-means clustering is used to remove the cluster outlier noise of ArrayInSAR point cloud data. In order to verify the effectiveness, the proposed method is compared with other classical outlier noise removal methods. The experimental results show that the method proposed in this paper can effectively remove outlier noise in ArrayInSAR point cloud data and improve the quality of ArrayInSAR point clouds.