Tengfei Zhang

Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely on limited data sources and often neglect essential terrain features. To address the issues, SAR data complements existing sources with its all-weather capability and strong penetration, and a Transformer-based Residual Attention Network combining SAR and Terrain Features (TRAN-ST) is proposed. The network incorporates intensity and coherence as SAR features to restore the details of the high-resolution DEMs, while slope and aspect constraints in the loss function enhance terrain consistency. Additionally, it combines the lightweight Transformer module with the residual feature aggregation module, which enhances the global perception capability while aggregating local residual features, thereby improving the reconstruction accuracy and training efficiency. Experiments were conducted on two DEMs in San Diego, USA, and the results show that compared with methods such as the bicubic, SRCNN, EDSR, RFAN, HNCT methods, the model reduces the mean absolute error (MAE) by 2–30%, the root mean square error (RMSE) by 1–31%, and the MAE of the slope by 2–13%, and it reduces the number of parameters effectively, which proves that TRAN-ST outperforms current typical methods.

Digital elevation models (DEMs) are essential for national economic development, disaster management, and military applications. Multi baseline interferometric synthetic aperture radar (MB-InSAR) technology has proven to be an effective method for DEM reconstruction. However, the presence of atmospheric noise and other residual signals introduces unavoidable errors in the phase observations, and most MB-InSAR DEMs are generated using a single empirical mathematical model that ignores the influence of deformation factors. To compensate for these limitations, we propose spatial independent component analysis (sICA) phase separation and interferometric synthetic aperture radar (InSAR) combinatorial modeling (CM) InSAR CM (ISCM). The sICA was used for phase separation, resulting in clear InSAR signals and reducing atmospheric noise and other residual signal interference; then, the effects of linear deformation, seasonal deformation, and environmental factors were considered in the InSAR modeling. In the experiments, a total of 19 TerraSAR-X images from San Diego, USA (SD), and 18 PAZ images from Yan’an, China (YA), were selected to generate DEMs with resolutions of 3 and 6 m, respectively. The accuracy of the DEM generated by ISCM was evaluated using the photogrammetric DEM, and the root-mean-square errors (RMSEs) of the elevation are 3.20 m for SD and 4.41 m for YA, with an improvement of 30.8%–44.9% and 21.9%–38.4%, respectively, compared to the traditional MB-InSAR method. In addition, ICESat/GLAS data collected in YA were used for further validation with an improvement of 13.7%–29.5%. The DEM generated by ISCM has significant advantages in improving accuracy and preserving terrain features, providing theoretical support for global high-precision DEM mapping.

Synthetic aperture radar (SAR) systems can provide sub-meter terrain mapping and accurate point elevation information quickly and efficiently. InSAR technology has proven to be a powerful method for producing digital elevation models (DEMs). However, DEM generation using InSAR technology is limited by mountain shadow overlap, atmospheric noise, low backscatter coefficient, and spatiotemporal incoherence, leading to the problem of voids. This article proposes an intelligent learning reconfiguration model based on optimized transformer and multi-source features (TMSF). First, the intelligent learning reconfiguration model based on the transformer and convolutional neural network (CNN) was constructed, and the multi-source feature connection module was used for feature supervision and loss function optimization. Then, the relationship of non-void areas between the low-resolution DEM and the high-resolution InSAR DEM was found, and the voids were intelligently filled. The experiments used 19 TerraSAR-X images in San Diego (SD), USA, and 18 PAZ images in Yan’an (YA), China, to generate high-precision InSAR void DEMs and intelligently fill the voids. Compared to traditional interpolation or deep learning models, modeling accuracy improved by 11.31-45.74% and 2.32-8.78% in the SD and YA areas, respectively. Using the photogrammetric DEM to evaluate the accuracy of the filled DEM, the new method showed improvements of 15.64-25.91% and 5.60-28.26% respectively. In addition, 122 ICESat/GLAS points collected in the YA area were further validated, with an improvement of 4.40-22.28%. The generated DEM has considerable advantages for terrain feature preservation and river network extraction, and the new method can provide technical support for DEM void filling.

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas.

Long-term deformation prediction and quantitative brine estimation are of great significance for safety precautions and environmental protection in salt mining areas. Traditional Interferometric Synthetic Aperture Radar (InSAR) combined with Probability Integral Method (PIM) has the following limitations: mathematical empirical models are mostly used in InSAR deformation modelling, which ignore the mechanisms of underground mining thus may limit the accuracy of deformation observations; InSAR deformation models have theoretical contradictions and inconsistencies with the future prediction models; PIM is not applicable to the deformation induced by salt solution mining. In this study, a novel Water-Solution Kinetic (WSK) InSAR model was proposed to replace the traditional InSAR models. It considers the principles of substance diffusion and mass transfer of salt, thus can be used directly for the quantitative prediction of both the deformations and extracted brine. Least Squares with Inequality Constraints (LSIC) was introduced to solve the unknown WSK parameters based on the InSAR phases to improve the computational efficiency. Two salt mines in China were selected for the experiments. The results show that the maximum predicted deformation was 296 mm in XR and 155 mm in HR, and the brine composition was estimated to be 0.406 and 1.477 million tons per year, respectively. Compared to traditional Static-PIM, the accuracy was improved by 48.3% and 54.5%, respectively. The results indicated that WSK is more precise and reasonable for predicting the deformation in salt mining areas, which can provide data reference for the management of salt mines.

Objectives: The Airport South Expressway in China is built in a soft soil area, which may induce great hidden danger to airport traffic safety operations. Technology or Method: A new method to improve multitemporal interferometric synthetic aperture radar (MT-InSAR) technology, based on a novel time-series InSAR deformation model and an improved parameter estimation algorithm, is proposed for soft soil expressways monitoring. The functional relationship between the deformation and the creep parameters (viscosity and elastic modulus) based on the Maxwell model in 1-D linear rheology replaces the traditional InSAR linear model, and the creep physical parameters can be solved simultaneously in the solution process. The least squares method with inequality constraints (LSICs) is induced to solve the unknown parameters. In total, 19 TerraSAR-X radar satellite images covering the South Expressway were utilized to validate the proposed method. The creep parameters for each pixel along the expressways and the time-series deformation sequences from January 2012 to July 2014 were obtained. Results: As the results showed, the maximum settlement along the expressway was up to 125 mm, and the accuracy verification results showed that the modeling accuracy was 1.6 mm, with an improvement of 36.0% compared to the traditional linear model; the internal accuracy of the deformation results was ±1.9 mm, accounting for 1.5% of the maximum deformation. Clinical or Biological Impact: Our method can provide data support and a reference for long-term health monitoring and early warning of infrastructure and traffic operation management in poor soil regions.

Landfast ice has undergone a dramatic decline in recent decades, imposing potential effects on ice travel for coastal populations, habitats for marine biota, and ice use for industries. The mapping of landfast ice deformation and the investigation of corresponding causes of changes are urgent tasks that can provide substantial data to support the maintenance of the stability of the Arctic ecosystem and the development of human activities on ice. This work aims to investigate the time-series deformation characteristics of landfast ice at multi-year scales and the corresponding influence factors. For the landfast ice deformation monitoring technique, we first combined the small baseline subset approach with ascending and descending Sentinel-1 images to obtain the line-of-sight deformations for two flight directions, and then we derived the 2D deformation fields comprising the vertical and horizontal directions for the corresponding periods by introducing a transform model. The vertical deformation results were mostly within the interval [−65, 23] cm, while the horizontal displacement was largely within the range of [−26, 78] cm. Moreover, the magnitude of deformation observed in 2019 was evidently greater than those in 2020 and 2021. In accordance with the available data, we speculate that the westerly wind and eastward-flowing ocean currents are the dominant reasons for the variation in the horizontal direction in Cambridge Bay, while the factors causing spatial differences in the vertical direction are the sea-level tilt and ice growth. For the interannual variation, the leading cause is the difference in sea-level tilt. These results can assist in predicting the future deformation of landfast ice and provide a reference for on-ice activities.

Deformation prediction for a salt solution mining area is essential to mining environmental protection. The combination of Synthetic Aperture Radar Interferometry (InSAR) technique with Probability Integral Method (PIM) has proven to be powerful in predicting mining-induced subsidence. However, traditional mathematical empirical models (such as linear model or linear model combined with periodical function) are mostly used in InSAR approaches, ignoring the underground mining mechanisms, which may limit the accuracy of the retrieved deformations. Inaccurate InSAR deformations will transmit an unavoidable error to the estimated PIM parameters and the forward predicted subsidence, which may induce more significant errors. Besides, theoretical contradictory and non-consistency between InSAR deformation model and future prediction model is another limitation. This paper introduces the Coordinate-Time (CT) function into InSAR deformation modeling. A novel time-series InSAR model (namely, CT-PIM) is proposed as a substitute for traditional InSAR mathematical empirical models and directly applied for future dynamic prediction. The unknown CT-PIM parameters can be estimated directly via InSAR phase observations, which can avoid the error propagation from the InSAR-generated deformations. The new approach has been tested by both simulated and real data experiments over a salt mine in China. The root mean square error (RMSE) is determined as ±10.9 mm, with an improvement of 37.2% compared to traditional static PIM prediction method. The new approach provides a more robust tool for the forecasting of mining-induced hazards in salt solution mining areas, as well as a reference for ensuring the environment protection and safety management.