This article proposes deep convolutional neural networks to detect and map localized, rapid subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar (SAR) images. A deformation detection network (DDNet) is developed to automatically identify rapidly subsiding areas from wrapped interferograms, and a phase unwrapping network (PUNet) is designed to unwrap the cropped interferogram patches centered on the detected subsiding locations. To train the two networks, interferogram simulation strategies are developed to generate various training samples using the distorted 2-D Gaussian surface and fractal Perlin noises. The performance of the DDNet is verified by simulations on a synthetic dataset with 13 large interferograms, while the PUNet is evaluated by simulations using synthetic datasets with different levels of deformation gradients and noises. Compared with the traditional and deep-learning methods, the PUNet exhibits excellent performance and efficiency in unwrapping interferograms with rapid mining-induced deformation. The proposed networks are further verified by applying them to Shanxi province, China, which is characterized by serious ground subsidence hazards caused by long-term coal mining activities. The time-series deformations of 1344 detected subsidence areas are calculated with the vertical velocities ranging from −19.7 to −254.8 cm/year. The results are validated using the ascending and descending Sentinel-1 Interferograms and an L-band ALOS-2 interferogram covering the same area within the acquisition period, showing highly consistent vertical deformation rates. The proposed strategy and methods introduce deep learning to the time-series interferometric SAR (InSAR) processing chain and may have profound implications on the detection and monitoring of localized mining-induced deformation using InSAR.

Phase unwrapping is a critical step of interferometric synthetic aperture radar processing, and its accuracy directly determines the reliability of subsequent applications. Many phase unwrapping methods have been proposed, most of which assume that the phase has spatial continuity, while decorrelation noise and aliasing fringes invalidate the assumptions, resulting in poor performance of these methods. To obtain more reliable unwrapping results, in this article, a deep convolutional neural network, called a discontinuity estimation network (DENet), is proposed for predicting the probabilities of phase discontinuities in interferograms. The main advantages of DENet are: 1) using branching structure to extract detailed and high-level features separately and retain details while making full use of contextual information; 2) using multichannel input, including interferogram, range/azimuthal phase gradients, and residues map, to provide effective guidance for discontinuity prediction; and 3) using a single network to estimate phase discontinuities in both range and azimuth directions simultaneously. To train the network, a dataset simulation strategy is proposed to generate enough training samples. The strategy considers a variety of phase components, such as terrain-related phase, random deformation, atmospheric turbulence, and noise. The phase discontinuity estimated by DENet is then converted to costs in the minimum cost flow (MCF) solver of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU) to obtain the final unwrapped phase. Based on validations of simulated and real interferograms, the proposed method exhibits excellent performance compared to traditional and deep learning unwrapping methods. The proposed method can effectively unwrap large-scale, low-quality interferograms, which is expected to significantly improve the accuracy of synthetic aperture radar interferometry (InSAR) applications.

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.

Accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this paper, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this paper, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-earth phase based baseline refinement methods are made. The results indicated superior accuracy and robustness of our method especially in areas with higher relief and wider coverage.

Mining activities may cause severe ground subsidence, endangering surface structures and farmlands. Therefore, the acquisition of spatial-temporal evolution of the mining goaf is of great significance. Hotspot analysis (HSA) based on the Getis-Ord G $_{i}^*$ statistics has been utilized to identify the areas with a rapid deformation rate. In this paper, we propose an improved HSA (IHSA) method for automatic extraction of the surface subsidence caused by the mining goaf. Additionally, we design a comprehensive workflow for the automatic spatial-temporal evolution inversion of surface deformation induced by the mining goaf. Firstly, time series interferometric synthetic aperture radar (TS-InSAR) is utilized to generate the surface deformation of the mining area. Then, the IHSA method is used for the automatic identification of the mining goaf. Finally, the total least-squares probability integral method (TLS-PIM) is applied for goaf inversion based on the extracted deformation information. For this study, the Wuxiang is selected as the study area. We have compared the IHSA method with four methods using five indicators, and likewise, we have compared the TLS-PIM method with four methods in terms of their correlation with the InSAR results in the strike and dip directions. The experimental results demonstrate the superiority of our method as a support for the geological hazard investigation and mine safety supervision department.