Ground‐based synthetic aperture radar (GB‐SAR) is a relatively new technique that can be used to monitor the deformation of large‐volume targets, such as dams, slopes, and bridges. In this study, the permanent scatterer (PS) technique is used to address the issues encountered in the continuous monitoring of the external deformation of an arch‐gravity dam in a hydraulic and hydropower engineering structure in Hubei, China; the technique includes large image data sizes, high accuracy requirements, a susceptibility of the monitoring data to atmospheric disturbances, complex phase unwrapping, and pronounced decoherence. Through an in‐depth investigation of PS extraction methods, a combined PS selection (CPSS) method is proposed by fully taking advantage of the signal amplitude and phase information in the monitored scene. The principle and implementation of CPSS are primarily studied. In addition, preliminarily selected PS candidates are directly used to construct and update a triangular irregular network (TIN) to maintain the stability of the subsequent Delaunay TIN. To implement this method, a differential‐phase standard‐deviation threshold method is proposed to extract PSs that are highly spatially coherent and consistent. Finally, the proposed CPSS was applied to the safety monitoring of the dam. The monitoring results are compared with conventional inverted plumb line monitoring results, and the proposed CPSS is found to be effective and reliable.

The ground-based Synthetic Aperture Radar (GB-SAR) technique can be applied to the safety monitoring and early warning of geo-hazards, especially for monitoring displacement of various types of landslide masses. One of the key techniques of processing GB-SAR monitoring data is phase unwrapping, which can be dramatically affected by atmospheric humidity, atmospheric pressure, sampling interval, etc. In high mountains and valleys where environmental change is drastic, GB-SAR monitoring data is vulnerable to incoherence both spatially and temporally. Therefore, an improved phase unwrapping method of GB-SAR data for landslide monitoring is proposed in this paper, which can realize three-dimensional phase unwrapping in time and space. The method adopts the idea of sparse data processing and realizes phase unwrapping of monitoring data in two steps. Firstly, taking full advantage of the high temporal resolution of GB-SAR monitoring data, the one-dimensional phase unwrapping method in the spatial domain is applied to the time domain, and the interference phases of PS points are unwrapped in the time domain. Then, the PS network is constructed based on the time-phase unwrapping results, the double-difference phases of the network baselines are obtained and the spatial consistency adjustment is applied to them, and the spatial phase unwrapping can be realized by the indirect least square adjustment method. This method successfully solves the problem of path dependence, island isolation and large computation of the common method of space-borne SAR phase unwrapping, which can process GB-SAR data with high accuracy and efficiency.

Abnormal observations in historical dam-monitoring records can distort conventional support vector regression (SVR), while fixed residual thresholds cannot follow local temporal variations. To address these two problems, this study develops a robust SVR-based outlier-detection framework with observation weighting and temporal adaptation. The prediction stage does not directly employ conventional SVR. Instead, a segmented Huber-Danish (HD) reliability function is embedded in a weighted iterative SVR fitting procedure. Observation weights are updated from robust standardized residuals so that samples consistent with the prevailing relationship retain their influence, whereas observations with moderate and extreme residuals are progressively suppressed. Particle swarm optimization (PSO) is used as a supporting parameter-selection tool to coordinate the SVR hyperparameters and the segmentation parameter, yielding the HD-PSO-weighted iterative support vector regression predictor. The resulting residuals are subsequently screened using a time-weighted (TW) criterion. The criterion-level development integrates a sliding time window, temporal-distance weighting, and a hybrid weighted-mean-unweighted-standard-deviation strategy to construct locally adaptive detection intervals. Validation on contaminated dam-monitoring sequences shows that the proposed predictor maintains stable fitting performance as the contamination ratio increases and outperforms Bayesian regression, multilayer perceptron, recurrent neural network, and conventional SVR in resistance to abnormal observations. Comparisons with the interquartile range, Pauta, rolling Z -score, and rolling median absolute deviation criteria further demonstrate that the TW stage achieves a better balance between precision, recall, and false-alarm control. The framework is therefore suitable for robust data-quality screening in non-stationary dam-monitoring records.