Repositioning error (RE) compensation is one of the key steps in discontinuous ground-based synthetic aperture radar (D-GBSAR) monitoring. The traditional RE compensation is to perform 2-D phase unwrapping, and then remove the RE based on the least squares method, thereby introducing an extra unwrapping error. Specifically, due to the phase wrapped of the discrete permanent scatterers (PS), the least squares method is intractable to be performed directly. Hence, the core idea of this paper is to propose a new likelihood function model, and straightforwardly estimate the baseline parameters to compensate for the RE, avoiding the phase unwrapping. Firstly, we transform the discrete PS phase wrapped into a continuous function model, reducing the complexity of mathematical analysis. Then, based on the novel RE model in the context of Gaussian white noise, we obtain a concise mathematical expression of the Cramer-Rao lower bound (CRLB) for maximum likelihood estimation, which serves as the performance indicator for baseline estimation. Afterward, by introducing the Newton iteration method, we obtain the baseline estimation results and integrate a novel RE compensation deformation inversion processing methodology for D-GBSAR, named maximum likelihood-Newton iteration-RE compensation algorithm (MLNIRECA). Last but not least, the effectiveness of the proposed method is verified through simulation and real data experiments, where the root mean square error is constantly close to the CRLB with the increase of signal-to-noise ratio (SNR) when the SNR is greater than -10 dB. Particularly, we can extend the spatial baseline to 100 mm under the condition of accuracy requirements, and employ the proposed methodology to achieve sub-millimeter deformation monitoring accuracy over actual scenarios in time and space.

In order to compensate for the repositioning error introduced by the radar position offset in discontinuous GBSAR monitoring, a new mathematical framework for modeling the baseline error based on the Taylor expansion is developed in this letter. And then, a novel three-dimensional model called Multiparameter Nonlinear Trigonometric Model (MNTM) is proposed to accurately compensate for the repositioning error. Furthermore, to improve the compensating efficiency, we further develop an efficient two-dimensional model called Linear Trigonometric Model (LTM). Both simulation and field experiments verify the superiority and feasibility of the proposed methods, which measure the displacement with sub-millimeter accuracy.

The SAR–optical image matching is a research hotspot in the field of remote sensing. In this letter, we propose an end-to-end learning-based SAR–optical image matching algorithm. Initially, the algorithm applies local normalization filter (LNF) on multimodel image pairs and then detects keypoints with high repeatability and ease of matching via a self-supervised keypoint detection network. The detected keypoints are fed into our proposed Transformer–CNN dual-branch feature description Siamese network, extracting global and local contextual information of images to gain robust feature descriptors. In the training phase, we adopt a two-stage training strategy in the form of description then detection, which enables the keypoint detection network to learn in conjunction with the output of the feature description network, so as to obtain more robust keypoints. Experimental results show that our method achieves the average root mean square error (aRMSE) of 3.37 and 2.98 pixels on the test data, outperforming the three compared existing advanced algorithms.

As a new deformation monitoring method, Discontinuous Ground-Based Synthetic Aperture Radar (D-GBSAR) monitoring has gradually attracted people’s attention, and the key problem it faces is how to compensate for the repositioning error caused by radar position offset. To address the above issue, this paper studies the geometric relationship between radar spatial baseline and target, and proposes a novel repositioning error compensation method based on trigonometric model and least squares parameter estimation. The feasibility of the proposed method is verified by the measured data of buildings monitoring, based on the permanent scatterer interferometry.

The geometric positioning accuracy of synthetic aperture radar (SAR) images is affected by the radar system measurement and processing parameters, platform ephemeris data and auxiliary DEM data. Geometric calibration can compensate for the SAR systematic parameter errors to produce high-precision SAR products. SAR images of mountainous areas are difficult to be used for geometric calibration due to severe geometric distortion and difficulty in deploying and extracting control points. In this paper, a novel and effective geometric calibration method for rugged terrain is proposed. The proposed method exploits the unique geometric and radiometric distortion features of SAR images. Based on the energy variation of SAR orthophoto, the calibration of range and azimuth parameters is achieved by searching for the fast time and slow time offsets that can compensate for the systematic errors. Experiments based on real SAR images from LuTan-1 satellite were conducted. The proposed method shows good performance in the experiments. After geometric calibration, the root mean square error of LuTan-1 L1A images is reduced from 80.77 m to 6.39 m, which verifies that the proposed method can improve the positioning accuracy of SAR images. The proposed method is simple to realize and does not rely on additional control point data, which provides a new solution for the maintenance and parameter calibration of SAR satellites.

Complex construction environments can interfere with Ground-Based Synthetic Aperture Radar (GB-SAR) deformation monitoring, potentially leading to missed or false alarms. Current research on addressing engineering interference in GB-SAR deformation monitoring remains preliminary, with existing methods exhibiting limitations in interference pattern coverage, feature extraction, and algorithm robustness. To address these challenges, this paper proposes a joint processing method integrating amplitude-phase feature analysis and robust regression. The method first constructs a multi-threshold detection framework in the amplitude-phase dual-domain and combines the wavelet-Monte Carlo confidence screening strategy to achieve precise identification of engineering interference. Subsequently, a two-stage suppression model based on robust estimation theory is developed to effectively suppress interference. Experimental results from the Dabao Mountain and Pearl River mining areas demonstrate that the proposed method achieves a reduction of over 80% in the maximum standard deviation of the time-series cumulative phase after suppression, significantly improving phase stability. These findings validate the applicability and effectiveness of the proposed method in complex construction environments, providing strong support for the practical application of GB-SAR in engineering monitoring.