An accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for the 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 article, 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 article, 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 the superior accuracy and robustness of our method, especially in areas with higher relief and wider coverage.

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.

Interferometric synthetic aperture radar (InSAR) technique can periodically measure small deformation over a large area, providing many supports for landslide studies. However, landslide detection with InSAR is still a challenge problem, especially in the efficiency over large-area. In this paper, the workflow of InSAR driven landslide detection and monitoring is optimized based on small baseline sets. It adopts a two-step strategy: (1) regional detection by small baseline InSAR-stacking, (2) continuous monitoring of detected landslides by small baseline sets InSAR (SBAS-InSAR), making a trade-off between efficiency and accuracy. This workflow was applied in the Dongchuan Section of Jinsha River Valley. Over more than 5000 km2, 62 active landslides are detected and mapped. Then for one typical landslide, Dapingdi landslide, deformation time-series are estimated to illustrate its growing process.

Phase unwrapping is a crucial step of InSAR, and its reliability directly determines the feasibility of deformation monitoring. However, severe noise and dense fringes often make the existing unwrapping methods fail. In this work, we propose a convolutional neural network DENet for identifying phase discontinuities and design a data set simulation strategy to generate enough training samples. We combine the traditional cost flow method with the output from DENet to achieve more accurate phase unwrapping. Compared with the GAMMA, the root mean square error of the proposed method on the simulated data set is reduced by 46.4%. We also verified the superior performance of the proposed method on real data sets.

A major landslide in Nanyu township, Zhouqu county, Western China, occurred on July 12, 2018, causing widespread damage and blocking the Bailong river. As a widely used surface deformation measurement technique, persistent scatterer interferometry (PSI) method fails in the precursory slope deformation detection over Nanyu landslide for two reasons: the high rate slope deformation of the landslide body before the occurrence of the disaster and the low density of measurement points in the widespread nonurban regions over hillside area. To solve these problems, a two-step multitemporal interferometric synthetic aperture radar (MTInSAR) method is proposed in this letter. In the proposed method, first, a carefully designed small baseline subsets (SBASs) method is applied to extract the linear high rate deformation component, which is then subtracted from the wrapping interferogram to reduce the likelihood of signal aliasing. In the second step, a two-tier network-based MTInSAR method by exploiting both persistent scatterers (PSs) and distributed scatterers (DSs) is employed to retrieve the residual deformation. The proposed method is applied to 47 Sentinel-1 images acquired between December 2016 and July 2018 and successfully detects the precursory deformation over Nanyu landslide for the first time. Experiment results illustrate the advantage of the proposed method for measuring high rate deformation compared with the traditional PSI. By using the proposed algorithm, the deformation rate map shows a clear landslide boundary and precursors of the rapid slope movements with a maximum displacement rate of 483 mm/year along the line of sight of the satellite.

Mining induced subsidence seriously damages the ecological environment and may cause casualties. Therefore, the rapid and reliable monitoring is particularly important. However, due to severe noise and dense fringes, traditional InSAR methods often severely underestimate the deformation rate. Here, we propose a new processing flow and develop two deep convolutional neural networks for fast detection and phase unwrapping of local subsidence cones. The proposed method is applied to Datong City, Shanxi Province, which is rich in mining activates. The processing results verify the reliability of the method.

Landslide, the main geological hazards, needs to be periodically monitored for disaster forecasting and prevention. An effective method to do this is to apply the offset tracking technique on SAR satellite data. Successful applications of this technique depend on the appropriate estimation window. However as a shortage, the selection of estimation window often relies on rich processing experience or multiple attempts. In this paper, we proposed an optimal estimation window offset tracking (OEW offset tracking) technique. As an improvement, this technique provides a theoretical basis and a work-flow to design the optimal estimation window for offset tracking, considering both measurement accuracy and processing efficiency. This technique was applied to the Baige landslide, based on GF-3 data and achieved successful results.

The multitemporal interferometric synthetic aperture radar (InSAR) technique is a potential tool for measuring digital elevation models and surface deformation. It has the advantage of high precision, competitive spatial resolution, and wide coverage. To improve the accuracy of the final results, some adaptive multilook strategies have been proposed in which the identification of statistically homogeneous pixels (SHPs) is the key task. However, these methods are not always reliable in the case of small data sets. To improve this reliability, SHPs are identified based on the adaptive joint data vector comprising of temporal sample and spatial information in this letter. Additionally, the formulation of adaptive joint data vector is combined with local spatial features of SAR images. The presented adaptive multilook approach can be used in many interferometric applications, such as InSAR data filtering and coherence estimation. Experiments on six TerraSAR-X stripmap images of Tianjin in China validate the feasibility and effectiveness of the proposed approach.

It has been proven that SqueeSAR technique has validated the potential to increase the density of measure points (MP) involving persistent scatter (PS) and distributed scatter (DS) candidate in nonurban areas. In SqueeSAR, DS candidate exhibiting high extended temporal coherence can be processed jointly with PS for deformation estimation after phase estimation by the phase triangulation algorithm (PTA). The PTA extracts phase values of DS by using all possible interferograms under the Gaussian scattering assumption. However, the statistics hypothesis, Gaussian random variable, is no more applicable, with radar resolution increasing. More precisely, it has been shown that clutter in high resolution SAR images can be modeled as a compound Gaussian process. In this letter, a modified approach to phase estimation of DS named MPTA is proposed based on the compound Gaussian model. Experiments on real data are presented to demonstrate the effectiveness of the proposed method.

Statistically Homogeneous Pixels (SHPs) selection is a significant step of multi-temporal interferometric synthetic aperture radar (InSAR) for Distributed Scatterers (DS). A series of studies namely, Anderson-Darling test (AD test) and its variants, have demonstrated their advantages. However, these algorithms have a similar drawback that they put little attention on the spatial amplitude distributions and cost too much time of processing. To solve the problem, this paper proposes a modified statistically homogeneous pixels selection algorithm (MoSHPS). It utilizes the amplitude values to get the prior information of the images through an unsupervised classifier, in order to guide the SHPs selection. It can improve the accuracy for SHPs selection, and promote the computing efficiency. In the end, results on a series of real TerraSAR-X datas, acquired over a Tianjin area, confirm the effectiveness of this algorithm.

Statistically homogeneous pixels (SHPs) are considerably significant in many interferometric applications, such as interferometric filtering, distributed scatterer selection, small baseline subset, and SqueeSAR processing. It is very important to achieve SHPs efficiently and accurately. Previous studies on SHPs' selection are based on spatial restrictions and likelihood ratio test, such as Lee filtering, Kolmogorov-Smirnov test, and Anderson-Darling test. However, these algorithms do not stand up to test for the spatial similarity hypothesis for complex terrains with a few images. To solve the problems, this letter proposes a modified SHPs' selection algorithm. It utilizes geometric distance and target features for reaching a priori information to help the similarity hypothesis tests. The proposed algorithm has been tested on simulated and real data to prove the improvements in terms of accuracy and computational efficiency.