The selection of statistically homogeneous pixels (SHPs) is essential for precise deformation monitoring in distributed scatterer synthetic aperture radar interferometry (DS-InSAR). Current SHP selection methods face challenges in test efficacy under small-sample and low-contrast conditions, resulting in imbalanced Type I and Type II errors and poor detection of weak heterogeneity. To address these issues, the wide-scope homogeneous testing (WSHT) algorithm is introduced, which enhances the hypothesis test of confidence interval (HTCI) by calculating the extremes of the confidence interval length and integrating Baumgartner-Weiss–Schindler (BWS) to minimize this length and improve the accuracy of the reference pixel mean. Simulations demonstrate that WSHT outperforms BWS and HTCI, achieving accuracy improvements of 71.00% and 34.71%, respectively. Analysis of Sentinel-1 images from Shenzhen City further highlights WSHT’s performance, achieving the highest SNR of 0.2755, a balanced speckle suppression index (SSI) of 5.5486, and the lowest mean squared error (MSE) of 0.2089, outperforming BWS and HTCI in noise suppression, resolution preservation, and robustness to sample size variations.

Sensor errors, platform ephemeris errors, and auxiliary digital elevation model (DEM) errors can have an impact on the positioning accuracy of synthetic aperture radar (SAR) images. Utilizing corner reflectors for geometric calibration is a common way to improve parameter accuracy and therefore positioning accuracy. In this article, we propose an energy-based geometric self-calibration method without relying on corner reflectors. Based on the radiometric and geometric properties of SAR images, the energy of SAR orthophoto over the rugged mountainous areas is taken as objective function. The conjugate gradient method is used to estimate fast time offset and slow time offset by maximizing the objective function, which achieves the equivalent compensation for positioning errors. SAR images from Radarsat-2, COSMO-SkyMed, TerraSAR-X, GaoFen-3, LuTan-1 and ChaoHu-1 satellites were used for the experiments, and the experimental results demonstrate the effectiveness and applicability of the proposed method. The accuracy evaluation results of corner reflectors and field-measured checkpoints show that the proposed method improves the positioning errors of SAR images from different satellites from tens of meters to less than 10 m. Our proposed method not only provides a new perspective on the geometric calibration of SAR but also reduces the maintenance cost and the workload of external calibration during the daily operation of spaceborne SAR, which is of great significance for low-cost commercial satellites.

Discontinuous ground-based synthetic aperture radar monitoring (D-GBSAR) has gained increasing attention in the last five years, while the full processing framework has not been significantly reported. In this paper, a new full framework for D-GBSAR is presented, in which we integrate the advanced technique of image registration, permanent scatterer (PS) selection, phase filtering, repositioning error, and atmospheric phase compensation. Particularly, we reveal that the sensor’s spatial baseline is small thus making it relatively simple to registrate the images. Furthermore, we apply complex mean filtering to mitigate the stochastic noise for better performance. Thereafter, we utilize the newly proposed methods to perform the azimuth-based repositioning error and slant distance-based atmospheric phase compensation. Finally, typical experiments verify the effectiveness of the proposed method, which is comparable with the advanced method and showcases an important technical reference for D-GBSAR applications.

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 on multi-model 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.

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.

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.

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.

5G New Radio (NR) introduces a new Radio Resource Control (RRC) state, i.e., RRC INACTIVE, for providing the efficient service for massive Machine Type Communications (mMTC). To release the full potential of the new RRC state, it is of great importance to properly model the new RRC state transition process and reveal the effect of system parameters on the network performance. To address the above issue, this paper proposes a novel 5G RRC analytical model based on discrete-time vacation queuing theory, where the time period of the device in RRC INACTIVE state is regarded as the vacation period of the server in the queueing system. By leveraging this novel model, key performance metrics, such as the random access rate and the RRC resource utilization ratio, are explicitly characterized and obtained as functions of system parameters, including packet arrival rate, service rate and inactivity timer. The analysis reveals that to reduce the random access rate, the system should increase the inactivity timer, packet arrival rate or decrease the service rate. On the other hand, to improve the RRC resource utilization ratio, the inactivity timer should be cut down especially when the arrival rate is small or the service rate is large. The analysis is verified by simulations and sheds important light on practical 5G network design for supporting mMTC.